Tootfinder

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
Younghan Lee, Yungi Cho, Woorim Han, Ho Bae, Yunheung Paek
https://arxiv.org/abs/2403.02846

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
Federated Learning (FL) thrives in training a global model with numerous clients by only sharing the parameters of their local models trained with their private training datasets. Therefore, without revealing the private dataset, the clients can obtain a deep learning (DL) model with high performance. However, recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model when adversaries, posed as benign clients, are present in a group of clients.…

Federated Learning Using Coupled Tensor Train Decomposition
Xiangtao Zhang, Eleftherios Kofidis, Ce Zhu, Le Zhang, Yipeng Liu
https://arxiv.org/abs/2403.02898

Federated Learning Using Coupled Tensor Train Decomposition
Coupled tensor decomposition (CTD) can extract joint features from multimodal data in various applications. It can be employed for federated learning networks with data confidentiality. Federated CTD achieves data privacy protection by sharing common features and keeping individual features. However, traditional CTD schemes based on canonical polyadic decomposition (CPD) may suffer from low computational efficiency and heavy communication costs. Inspired by the efficient tensor train decomposit…

Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
Ehsan Nowroozi, Imran Haider, Rahim Taheri, Mauro Conti
https://arxiv.org/abs/2403.02983

Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficul…

Spectrum Occupancy Detection Supported by Federated Learning
{\L}ukasz Ku{\l}acz
https://arxiv.org/abs/2403.03617 https://arxiv.org/p…

Spectrum Occupancy Detection Supported by Federated Learning
Dynamic spectrum access is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is effective spectrum occupancy detection. In many cases, machine learning algorithms improve detection effectiveness. Because of the recent trend of using federated learning, a federated learning algorithm is presented in the context of distributed spectrum occupancy detection. The results of the work presented in the paper are based on actual signa…

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
https://arxiv.org/abs/2405.01961

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks
Federated Reinforcement Learning (FRL) offers a promising solution to various practical challenges in resource allocation for vehicle-to-everything (V2X) networks. However, the data discrepancy among individual agents can significantly degrade the performance of FRL-based algorithms. To address this limitation, we exploit the node-wise invariance property of ReLU-activated neural networks, with the aim of reducing data discrepancy to improve learning performance. Based on this property, we intr…

A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems
Maeshal Hijazi, Payman Dehghanian
https://arxiv.org/abs/2403.03126

A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems
Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data susceptible to cyber threats and communication issues when transmitting data to a central server for training a single TSA model. Additional…

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Yichang Xu, Ming Yin, Minghong Fang, Neil Zhenqiang Gong
https://arxiv.org/abs/2403.03149

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack. In this work, we bridge the gap by proposing I…

Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
Xiaoxue Yu, Xingfu Yi, Rongpeng Li, Fei Wang, Chenghui Peng, Zhifeng Zhao, Honggang Zhang
https://arxiv.org/abs/2405.03372

Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
In the evolution towards 6G, integrating Artificial Intelligence (AI) with advanced network infrastructure emerges as a pivotal strategy for enhancing network intelligence and resource utilization. Existing distributed learning frameworks like Federated Learning and Split Learning often struggle with significant challenges in dynamic network environments including high synchronization demands, costly communication overheads, severe computing resource consumption, and data heterogeneity across n…

A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems
Maeshal Hijazi, Payman Dehghanian
https://arxiv.org/abs/2403.03126

A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems
Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data susceptible to cyber threats and communication issues when transmitting data to a central server for training a single TSA model. Additional…

Federated Multi-Agent Mapping for Planetary Exploration
Tiberiu-Ioan Szatmari, Abhishek Cauligi
https://arxiv.org/abs/2404.02289 https://

Federated Multi-Agent Mapping for Planetary Exploration
In multi-agent robotic exploration, managing and effectively utilizing the vast, heterogeneous data generated from dynamic environments poses a significant challenge. Federated learning (FL) is a promising approach for distributed mapping, addressing the challenges of decentralized data in collaborative learning. FL enables joint model training across multiple agents without requiring the centralization or sharing of raw data, overcoming bandwidth and storage constraints. Our approach leverages…

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
Yushen Lin, Kaidi Wang, Zhiguo Ding
https://arxiv.org/abs/2403.03157 https://

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Following that, solutions to address t…

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Osama Wehbi, Sarhad Arisdakessian, Mohsen Guizani, Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, Hoda Al khzaimi, Bassem Ouni
https://arxiv.org/abs/2405.00394

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the exist…

This https://arxiv.org/abs/2401.02880 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest majority among participants to withstand various attacks. In practice, however, servers are not always trusted, and an adversarial server can strategically select compromised clients to create a dishonest majority, thereby undermining the system's security guarantees. In this paper, we present Lotto, an FL system that addr…

Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization
Hanzhi Yu, Mingzhe Chen, Yuchen Liu
https://arxiv.org/abs/2403.00665

Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization
In this article, the use of channel state information (CSI) for indoor positioning is studied. In the considered model, a server equipped with several antennas sends pilot signals to users, while each user uses the received pilot signals to estimate channel states for user positioning. To this end, we formulate the positioning problem as an optimization problem aiming to minimize the gap between the estimated positions and the ground truth positions of users. To solve this problem, we design a …

This https://arxiv.org/abs/2307.02140 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to participate. To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users. We propose two reciprocal co…

This https://arxiv.org/abs/2404.02595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase trai…

Employing Federated Learning for Training Autonomous HVAC Systems
Fredrik Hagstr\"om, Vikas Garg, Fabricio Oliveira
https://arxiv.org/abs/2405.00389 h…

Employing Federated Learning for Training Autonomous HVAC Systems
Buildings account for 40 % of global energy consumption. A considerable portion of building energy consumption stems from heating, ventilation, and air conditioning (HVAC), and thus implementing smart, energy-efficient HVAC systems has the potential to significantly impact the course of climate change. In recent years, model-free reinforcement learning algorithms have been increasingly assessed for this purpose due to their ability to learn and adapt purely from experience. They have been shown…

Federated Learning for Blind Image Super-Resolution
Brian B. Moser, Ahmed Anwar, Federico Raue, Stanislav Frolov, Andreas Dengel
https://arxiv.org/abs/2404.17670 https://arxiv.org/pdf/2404.17670
arXiv:2404.17670v1 Announce Type: new
Abstract: Traditional blind image SR methods need to model real-world degradations precisely. Consequently, current research struggles with this dilemma by assuming idealized degradations, which leads to limited applicability to actual user data. Moreover, the ideal scenario - training models on data from the targeted user base - presents significant privacy concerns. To address both challenges, we propose to fuse image SR with federated learning, allowing real-world degradations to be directly learned from users without invading their privacy. Furthermore, it enables optimization across many devices without data centralization. As this fusion is underexplored, we introduce new benchmarks specifically designed to evaluate new SR methods in this federated setting. By doing so, we employ known degradation modeling techniques from SR research. However, rather than aiming to mirror real degradations, our benchmarks use these degradation models to simulate the variety of degradations found across clients within a distributed user base. This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research. Our proposed benchmarks investigate blind image SR under new aspects, namely differently distributed degradation types among users and varying user numbers. We believe new methods tested within these benchmarks will perform more similarly in an application, as the simulated scenario addresses the variety while federated learning enables the training on actual degradations.

Swarm Learning: A Survey of Concepts, Applications, and Trends
Elham Shammar, Xiaohui Cui, Mohammed A. A. Al-qaness
https://arxiv.org/abs/2405.00556 https:…

Swarm Learning: A Survey of Concepts, Applications, and Trends
Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic m…

Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Yuanming Shi, Li Zeng, Jingyang Zhu, Yong Zhou, Chunxiao Jiang, Khaled B. Letaief
https://arxiv.org/abs/2404.01875

Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the hig…

Distribution-Free Fair Federated Learning with Small Samples
Qichuan Yin, Junzhou Huang, Huaxiu Yao, Linjun Zhang
https://arxiv.org/abs/2402.16158 https://…

Distribution-Free Fair Federated Learning with Small Samples
As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic groups becomes critically important. However, most existing machine learning algorithms for ensuring fairness are designed for centralized data environments and generally require large-sample and distributional assumptions, underscoring the urgent need for fairness techniques adapted for decentralized and heterogeneous …

Queuing dynamics of asynchronous Federated Learning
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2405.00017 htt…

Queuing dynamics of asynchronous Federated Learning
We study asynchronous federated learning mechanisms with nodes having potentially different computational speeds. In such an environment, each node is allowed to work on models with potential delays and contribute to updates to the central server at its own pace. Existing analyses of such algorithms typically depend on intractable quantities such as the maximum node delay and do not consider the underlying queuing dynamics of the system. In this paper, we propose a non-uniform sampling scheme f…

Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators
Kilian Pfeiffer, Konstantinos Balaskas, Kostas Siozios, J\"org Henkel
https://arxiv.org/abs/2402.18569

Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators
In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tacked this problem on an algorithmic level, e.g., by letting constrained devices train a subset of the server …

LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game
Jianfeng Lu, Yue Chen, Shuqin Cao, Longbiao Chen, Wei Wang, Yun Xin
https://arxiv.org/abs/2405.00579

LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game
Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to alleviate communication burdens, its model performance will be degraded by non-IID data and limited communication resources. Current works often assume that data is uniformly distributed, which however contradicts the heterogeneity of IoT. Solutions of additional model training to check the data distribution inevitably increases computational costs and the risk of privacy leakage. The challenges in solving these issue…

This https://arxiv.org/abs/2403.03149 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack. In this work, we bridge the gap by proposing I…

Swarm Learning: A Survey of Concepts, Applications, and Trends
Elham Shammar, Xiaohui Cui, Mohammed A. A. Al-qaness
https://arxiv.org/abs/2405.00556 https:…

Swarm Learning: A Survey of Concepts, Applications, and Trends
Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic m…

This https://arxiv.org/abs/2306.08929 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the resilience of Collaborative Learning-based Recommender Systems Against Community Detection Attack
Collaborative-learning-based recommender systems emerged following the success of collaborative learning techniques such as Federated Learning (FL) and Gossip Learning (GL). In these systems, users participate in the training of a recommender system while maintaining their history of consumed items on their devices. While these solutions seemed appealing for preserving the privacy of the participants at first glance, recent studies have revealed that collaborative learning can be vulnerable to …

This https://arxiv.org/abs/2309.13024 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Zeroth-Order Federated Methods for Stochastic MPECs and Nondifferentiable Nonconvex Hierarchical Optimization
Motivated by the emergence of federated learning (FL), we design and analyze federated methods for addressing: (i) Nondifferentiable nonconvex optimization; (ii) Bilevel optimization; (iii) Minimax problems; and (iv) Two-stage stochastic mathematical programs with equilibrium constraints (2s-SMPEC). Research on these problems has been limited and afflicted by reliance on strong assumptions, including the need for differentiability of the implicit function and the absence of constraints in the l…

This https://arxiv.org/abs/2308.04466 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices. However, the decentralized learning paradigm and heterogeneity of FL further extend the attack surface for backdoor attacks. Existing FL attack and defense methodologies typically focus on the whole model. None of them recognizes the existence of backdoor-critical (BC) layers-a small subset of layers that dominate the model vulnerabilities. Attacking the BC layers a…

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Ajinkya Kiran Mulay, Xiaojun Lin
https://arxiv.org/abs/2402.19016

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions $p$ is much larger than the number of samples $n$. However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected. In particular, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when $p \gg n$, a…

This https://arxiv.org/abs/2308.04466 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices. However, the decentralized learning paradigm and heterogeneity of FL further extend the attack surface for backdoor attacks. Existing FL attack and defense methodologies typically focus on the whole model. None of them recognizes the existence of backdoor-critical (BC) layers-a small subset of layers that dominate the model vulnerabilities. Attacking the BC layers a…

Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning
Junjie Wu, Xuming Fang
https://arxiv.org/abs/2404.01638

Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning
As artificial intelligence (AI)-enabled wireless communication systems continue their evolution, distributed learning has gained widespread attention for its ability to offer enhanced data privacy protection, improved resource utilization, and enhanced fault tolerance within wireless communication applications. Federated learning further enhances the ability of resource coordination and model generalization across nodes based on the above foundation, enabling the realization of an AI-driven com…

Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks
Chunmei Xu, Shengheng Liu, Yongming Huang, Bjorn Ottersten, Dusit Niyato
https://arxiv.org/abs/2403.18946

Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks
At present, there is a trend to deploy ubiquitous artificial intelligence (AI) applications at the edge of the network. As a promising framework that enables secure edge intelligence, federated learning (FL) has received widespread attention, and over-the-air computing (AirComp) has been integrated to further improve the communication efficiency. In this paper, we consider a joint device selection and aggregate beamforming design with the objectives of minimizing the aggregate error and maximiz…

This https://arxiv.org/abs/2312.11489 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same …

This https://arxiv.org/abs/2312.06454 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs without gigabyte-size WSIs transportation and data privacy concerns. However, federated learning encounters challenges in addressing label imb…

FedQNN: Federated Learning using Quantum Neural Networks
Nouhaila Innan, Muhammad Al-Zafar Khan, Alberto Marchisio, Muhammad Shafique, Mohamed Bennai
https://arxiv.org/abs/2403.10861

FedQNN: Federated Learning using Quantum Neural Networks
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated l…

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Zhe Zhang, Ryumei Nakada, Linjun Zhang
https://arxiv.org/abs/2404.16287 <…

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Differentially private federated learning is crucial for maintaining privacy in distributed environments. This paper investigates the challenges of high-dimensional estimation and inference under the constraints of differential privacy. First, we study scenarios involving an untrusted central server, demonstrating the inherent difficulties of accurate estimation in high-dimensional problems. Our findings indicate that the tight minimax rates depends on the high-dimensionality of the data even w…

FedStruct: Federated Decoupled Learning over Interconnected Graphs
Javad Aliakbari, Johan \"Ostman, Alexandre Graell i Amat
https://arxiv.org/abs/2402.19163

FedStruct: Federated Decoupled Learning over Interconnected Graphs
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where inter-connections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or…

This https://arxiv.org/abs/2306.00038 has been replaced.
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FedCSD: A Federated Learning Based Approach for Code-Smell Detection
This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data privacy. These assertions have been supported by three experiments that have significantly leveraged three manually validated datasets aimed at detecting and examining different code smell scenarios. In experiment 1, which was concerned with a centralized training experiment, dataset two achieved the lowest accuracy (9…

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Bingnan Xiao, Jingjing Zhang, Wei Ni, Xin Wang
https://arxiv.org/abs/2404.14811

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper b…

Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
Mario Chahoud, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Mohsen Guizani
https://arxiv.org/abs/2405.00395 …

Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process…

FedMIL: Federated-Multiple Instance Learning for Video Analysis with Optimized DPP Scheduling
Ashish Bastola, Hao Wang, Xiwen Chen, Abolfazl Razi
https://arxiv.org/abs/2403.17331 …

FedMIL: Federated-Multiple Instance Learning for Video Analysis with Optimized DPP Scheduling
Many AI platforms, including traffic monitoring systems, use Federated Learning (FL) for decentralized sensor data processing for learning-based applications while preserving privacy and ensuring secured information transfer. On the other hand, applying supervised learning to large data samples, like high-resolution images requires intensive human labor to label different parts of a data sample. Multiple Instance Learning (MIL) alleviates this challenge by operating over labels assigned to the …

PackVFL: Efficient HE Packing for Vertical Federated Learning
Liu Yang, Shuowei Cai, Di Chai, Junxue Zhang, Han Tian, Yilun Jin, Kun Guo, Kai Chen, Qiang Yang
https://arxiv.org/abs/2405.00482

PackVFL: Efficient HE Packing for Vertical Federated Learning
As an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to data inflation and time-consuming operations. To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms. PackVFL packs multiple cleartexts into one ciphertext and supports single-instruction-multiple-data (SIMD)-style parallelism. We f…

Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
Hamid Reza Hashempour, Gilberto Berardinelli, Ramoni Adeogun, Shashi Raj Pandey
https://arxiv.org/abs/2404.18010

Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constraints. In the subnetworks, devices collaborate to train a global model by transmitting their local models at the edge-enabled primary access (pAP) directly or via secondary access points (sAPs), which act as relays to optimize the training latency. We begin by formulating the energy efficiency problem for our proposed …

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Zilinghan Li, Shilan He, Pranshu Chaturvedi, Volodymyr Kindratenko, Eliu A Huerta, Kibaek Kim, Ravi Madduri
https://arxiv.org/abs/2402.12271 <…

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. In this paper, we elaborate on the design of our Advanced Privacy-Preserving Federated Learning (APPFL) framework, which streamlines end-to-end secure and reliable federated learning experiments across cloud computing facilities and high-performance computing resources by lever…

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
https://arxiv.org/abs/2403.18375

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and a…

This https://arxiv.org/abs/2401.10375 has been replaced.
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Vulnerabilities of Foundation Model Integrated Federated Learning Under Adversarial Threats
Federated Learning (FL) addresses critical issues in machine learning related to data privacy and security, yet suffering from data insufficiency and imbalance under certain circumstances. The emergence of foundation models (FMs) offers potential solutions to the limitations of existing FL frameworks, e.g., by generating synthetic data for model initialization. However, due to the inherent safety concerns of FMs, integrating FMs into FL could introduce new risks, which remains largely unexplore…

This https://arxiv.org/abs/2301.03553 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

FedDebug: Systematic Debugging for Federated Learning Applications
In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers …

HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork
Xingyun Chen, Yan Huang, Zhenzhen Xie, Junjie Pang
https://arxiv.org/abs/2402.18445

HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork
There are still many challenges in Federated Learning (FL). First, during the model update process, the model parameters on the local user need to be sent to the server for aggregation. This involves the consumption of network bandwidth, especially when the number of users participating in FL is large. High communication costs may limit the application of FL in certain scenarios. Secondly, since users participating in FL usually have different data distributions, this heterogeneity of data may …

This https://arxiv.org/abs/2401.10375 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Vulnerabilities of Foundation Model Integrated Federated Learning Under Adversarial Threats
Federated Learning (FL) addresses critical issues in machine learning related to data privacy and security, yet suffering from data insufficiency and imbalance under certain circumstances. The emergence of foundation models (FMs) offers potential solutions to the limitations of existing FL frameworks, e.g., by generating synthetic data for model initialization. However, due to the inherent safety concerns of FMs, integrating FMs into FL could introduce new risks, which remains largely unexplore…

Federated reinforcement learning for robot motion planning with zero-shot generalization
Zhenyuan Yuan, Siyuan Xu, Minghui Zhu
https://arxiv.org/abs/2403.13245

Federated reinforcement learning for robot motion planning with zero-shot generalization
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the correspon…

Convergence Analysis of Split Federated Learning on Heterogeneous Data
Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu
https://arxiv.org/abs/2402.15166

Convergence Analysis of Split Federated Learning on Heterogeneous Data
Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server trains the other. Despite the recent research on SFL algorithm development, the convergence analysis of SFL is missing in the literature, and this paper aims to fill this gap. The analysis of SFL can be more challenging than that of federate…

This https://arxiv.org/abs/2302.14648 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Digital Over-the-Air Federated Learning in Multi-Antenna Systems
In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The P…

CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
Domenique Zipperling, Simeon Allmendinger, Lukas Struppek, Niklas K\"uhl
https://arxiv.org/abs/2402.19105

CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approaches like federated learning distribute the learning process but strain individual clients, especially with constrained resources (e.g., edge devices). In response to these challenges, we introduce CollaFuse, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusio…

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
Chengxi Li, Ming Xiao, Mikael Skoglund
https://arxiv.org/abs/2403.14905 ht…

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation proc…

Belt and Brace: When Federated Learning Meets Differential Privacy
Xuebin Ren, Shusen Yang, Cong Zhao, Julie McCann, Zongben Xu
https://arxiv.org/abs/2404.18814

Belt and Brace: When Federated Learning Meets Differential Privacy
Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees.Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achieving practically usable FL with DP, which however is still challenging.Practitioners often not only are not fully aware of its development a…

This https://arxiv.org/abs/2404.11536 has been replaced.
link: https://scholar.google.com/scholar?q=a

FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine-tune FM by allocating sub-FM to clients in FL, however, leading to suboptimal performance due to insufficient tuning and inevitable error accumulations of gradients. In this paper, we propose Federated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modu…

Membership Information Leakage in Federated Contrastive Learning
Kongyang Chen, Wenfeng Wang, Zixin Wang, Wangjun Zhang, Zhipeng Li, Yao Huang
https://arxiv.org/abs/2404.16850

Membership Information Leakage in Federated Contrastive Learning
Federated Contrastive Learning (FCL) represents a burgeoning approach for learning from decentralized unlabeled data while upholding data privacy. In FCL, participant clients collaborate in learning a global encoder using unlabeled data, which can serve as a versatile feature extractor for diverse downstream tasks. Nonetheless, FCL is susceptible to privacy risks, such as membership information leakage, stemming from its distributed nature, an aspect often overlooked in current solutions. This …

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Xuefeng Han, Wen Chen, Jun Li, Ming Ding, Qingqing Wu, Kang Wei, Xiumei Deng, Zhen Mei
https://arxiv.org/abs/2402.12957

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization…

FedFDP: Federated Learning with Fairness and Differential Privacy
Xinpeng Ling, Jie Fu, Zhili Chen, Kuncan Wang, Huifa Li, Tong Cheng, Guanying Xu, Qin Li
https://arxiv.org/abs/2402.16028

FedFDP: Federated Learning with Fairness and Differential Privacy
Federated learning (FL) is a new machine learning paradigm to overcome the challenge of data silos and has garnered significant attention. However, through our observations, a globally effective trained model may performance disparities in different clients. This implies that the jointly trained models by clients may lead to unfair outcomes. On the other hand, relevant studies indicate that the transmission of gradients or models in federated learning can also give rise to privacy leakage issue…

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
Yang Xu, Yunlin Tan, Cheng Zhang, Kai Chi, Peng Sun, Wenyuan Yang, Ju Ren, Hongbo Jiang, Yaoxue Zhang
https://arxiv.org/abs/2402.19054

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
Embedding watermarks into models has been widely used to protect model ownership in federated learning (FL). However, existing methods are inadequate for protecting the ownership of personalized models acquired by clients in personalized FL (PFL). This is due to the aggregation of the global model in PFL, resulting in conflicts over clients' private watermarks. Moreover, malicious clients may tamper with embedded watermarks to facilitate model leakage and evade accountability. This paper presen…

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Tak\'a\v{c}
https://arxiv.org/abs/2403.18444

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets using a hierarchical control architecture of reinforcement learning agents trained using federated learning. The core concept we are provin…

Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Mohak Chadha, Alexander Jensen, Jianfeng Gu, Osama Abboud, Michael Gerndt
https://arxiv.org/abs/2404.14033

Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, c…

Detection of ransomware attacks using federated learning based on the CNN model
Hong-Nhung Nguyen, Ha-Thanh Nguyen, Damien Lescos
https://arxiv.org/abs/2405.00418

Detection of ransomware attacks using federated learning based on the CNN model
Computing is still under a significant threat from ransomware, which necessitates prompt action to prevent it. Ransomware attacks can have a negative impact on how smart grids, particularly digital substations. In addition to examining a ransomware detection method using artificial intelligence (AI), this paper offers a ransomware attack modeling technique that targets the disrupted operation of a digital substation. The first, binary data is transformed into image data and fed into the convolu…

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Zifan Zhang, Minghong Fang, Jiayuan Huang, Yuchen Liu
https://arxiv.org/abs/2404.14389

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial auto…

This https://arxiv.org/abs/2311.04253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Blind Federated Learning via Over-the-Air q-QAM
In this work, we investigate federated edge learning over a fading multiple access channel. To alleviate the communication burden between the edge devices and the access point, we introduce a pioneering digital over-the-air computation strategy employing q-ary quadrature amplitude modulation, culminating in a low latency communication scheme. Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge s…

Generalized Policy Learning for Smart Grids: FL TRPO Approach
Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horv\'ath, Martin Tak\'a\v{c}
https://arxiv.org/abs/2403.18439

Generalized Policy Learning for Smart Grids: FL TRPO Approach
The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Tru…

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas
https://arxiv.org/abs/2404.13804

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive c…

Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Hanqing Fu, Gaolei Li, Jun Wu, Jianhua Li, Xi Lin, Kai Zhou, Yuchen Liu
https://arxiv.org/abs/2403.18607

Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks. The threat of backdoor attacks on traditional deep neural networks typically comes from time-invariant data. However, in FedNL, unknown threats may be hidden in time-varying spike signals. In this paper, we start to explore a nov…

This https://arxiv.org/abs/2311.06918 has been replaced.
link: https://scholar.google.com/scholar?q=a

Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks
Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request. A privacy-preserving method is desirable to learn how users' demands change over time. As such, this paper proposes a novel resource-aware hierarchical federated learning (RawHFL) solution to predict users' future content requests under the realistic assumptions that content requests are sporadic and users' datasets can only be updated based on the requested …

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao, Xiaomin Ouyang, Daniel Nata Nugraha, Bing Luo
https://arxiv.org/abs/2402.10464

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
We present FedKit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. FedKit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated training, and cross-platform model aggregation. Our FL workflow supports flexible machine learning operations (MLOps) in production, facilitating continuous model delivery and training. We have deployed FedKit in a real-world use case for health data analysis on university campuse…

This https://arxiv.org/abs/2312.05248 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Topology-Based Reconstruction Prevention for Decentralised Learning
Decentralised learning has recently gained traction as an alternative to federated learning in which both data and coordination are distributed over its users. To preserve data confidentiality, decentralised learning relies on differential privacy, multi-party computation, or a combination thereof. However, running multiple privacy-preserving summations in sequence may allow adversaries to perform reconstruction attacks. Unfortunately, current reconstruction countermeasures either cannot trivia…

How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study
Natalija Mitic, Apostolos Pyrgelis, Sinem Sav
https://arxiv.org/abs/2402.16087

How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study
In this paper, we address the problem of privacy-preserving hyperparameter (HP) tuning for cross-silo federated learning (FL). We first perform a comprehensive measurement study that benchmarks various HP strategies suitable for FL. Our benchmarks show that the optimal parameters of the FL server, e.g., the learning rate, can be accurately and efficiently tuned based on the HPs found by each client on its local data. We demonstrate that HP averaging is suitable for iid settings, while density-b…

Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
https://arxiv.org/abs/2402.14430 <…

Robust Training of Federated Models with Extremely Label Deficiency
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, …

Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi
https://arxiv.org/abs/2403.18144

Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However, despite demonstrating the effectiveness of these attacks in breaching privacy, prior wo…

FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed, Atta Ur Rahman
https://arxiv.org/abs/2402.09095

FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The non-independent and identically distributed (non-i.i.d.) nature of client data, coupled with constraints on client or edge devices, presents significant challenges in FL. Furthermore, learning across a high number of communication rounds can be risky and potentially unsafe for model exploitation. Traditional FL appro…

Data Poisoning Attacks in Gossip Learning
Alexandre PhamNPA, Maria Potop-ButucaruNPA, S\'ebastien TixeuilNPA, IUF, Serge FdidaNPA
https://arxiv.org/abs/2403.06583

Data Poisoning Attacks in Gossip Learning
Traditional machine learning systems were designed in a centralized manner. In such designs, the central entity maintains both the machine learning model and the data used to adjust the model's parameters. As data centralization yields privacy issues, Federated Learning was introduced to reduce data sharing and have a central server coordinate the learning of multiple devices. While Federated Learning is more decentralized, it still relies on a central entity that may fail or be subject to atta…

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
https://arxiv.org/abs/2402.10862

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world app…

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu, Xu Yuan, Li Chen, Nian-Feng Tzeng
https://arxiv.org/abs/2403.04144

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of thei…

Improving Group Connectivity for Generalization of Federated Deep Learning
Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Chao Wu
https://arxiv.org/abs/2402.18949 h…

Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning
Kaiping Cui, Xia Feng, Liangmin Wang, Haiqin Wu, Xiaoyu Zhang, Boris D\"udder
https://arxiv.org/abs/2402.15111

Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning
Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest research, Flamingo (S&P 2023), designed a share-transfer-based reusable secret key to support the server continuously performing multiple rounds…

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu, Xu Yuan, Li Chen, Nian-Feng Tzeng
https://arxiv.org/abs/2403.04144

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of thei…

Defending against Data Poisoning Attacks in Federated Learning via User Elimination
Nick Galanis
https://arxiv.org/abs/2404.12778 https://

Defending against Data Poisoning Attacks in Federated Learning via User Elimination
In the evolving landscape of Federated Learning (FL), a new type of attacks concerns the research community, namely Data Poisoning Attacks, which threaten the model integrity by maliciously altering training data. This paper introduces a novel defensive framework focused on the strategic elimination of adversarial users within a federated model. We detect those anomalies in the aggregation phase of the Federated Algorithm, by integrating metadata gathered by the local training instances with Di…

FedD2S: Personalized Data-Free Federated Knowledge Distillation
Kawa Atapour, S. Jamal Seyedmohammadi, Jamshid Abouei, Arash Mohammadi, Konstantinos N. Plataniotis
https://arxiv.org/abs/2402.10846

FedD2S: Personalized Data-Free Federated Knowledge Distillation
This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework. The model-drift issue, arising from the noniid nature of client data, often results in suboptimal personalization of a global model compared to locally trained models for each client. To tackle this challenge, we propose a novel approach named FedD2S for Personalized Federated Learning (pFL), leveraging knowledge distillation. FedD2S incorporates a deep-to-shallow layer-…

Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning
Liwei Wang, Jun Li, Wen Chen, Qingqing Wu, Ming Ding
https://arxiv.org/abs/2404.08324

Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning
Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. However, the frequent exchange of model parameters between clients and the central server can result in significant communication overhead during the FL training process. To solve this problem, this paper proposes a novel FL framework, the Model Aggregation with Layer Divergence Feedback mechanism (FedLDF). Specifically, we…

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
Ji Liu, Chunlu Chen, Yu Li, Lin Sun, Yulun Song, Jingbo Zhou, Bo Jing, Dejing Dou
https://arxiv.org/abs/2403.19178

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learnin…

FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
Duy Phuong Nguyen, J. Pablo Munoz, Ali Jannesari
https://arxiv.org/abs/2404.15182

FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
In the rapidly evolving field of artificial intelligence, multimodal models, e.g., integrating vision and language into visual-language models (VLMs), have become pivotal for many applications, ranging from image captioning to multimodal search engines. Among these models, the Contrastive Language-Image Pre-training (CLIP) model has demonstrated remarkable performance in understanding and generating nuanced relationships between text and images. However, the conventional training of such models…

Scheduling for On-Board Federated Learning with Satellite Clusters
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski
https://arxiv.org/abs/2402.09105

Scheduling for On-Board Federated Learning with Satellite Clusters
Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations connected with intra-orbit inter-satellite links. The proposed scheme utilizes the predictable visibility pattern between satellites and grou…

FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption
Cem Ata Baykara, Ali Burak \"Unal, Mete Akg\"un
https://arxiv.org/abs/2403.14428

FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption
Ensuring data privacy is a significant challenge for machine learning applications, not only during model training but also during evaluation. Federated learning has gained significant research interest in recent years as a result. Current research on federated learning primarily focuses on preserving privacy during the training phase. However, model evaluation has not been adequately addressed, despite the potential for significant privacy leaks during this phase as well. In this paper, we dem…

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
Yebo Wu, Li Li, Chunlin Tian, Chengzhong Xu
https://arxiv.org/abs/2404.13349

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training round, ProFL first trains the front blocks and safely freezes them after convergence. Training of the next block is then triggered. This process iterates until the training of the whole model is completed. In this way, the memory footprint is effectivel…

This https://arxiv.org/abs/2104.10561 has been replaced.
link: https://scholar.google.com/scholar?q=a

Turning Federated Learning Systems Into Covert Channels
Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients. These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data. The global model is then shared among all the participants which use it for local predictions. In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth co…

Momentum Approximation in Asynchronous Private Federated Learning
Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis
https://arxiv.org/abs/2402.09247 https:…

Momentum Approximation in Asynchronous Private Federated Learning
Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients. Meanwhile, momentum-based methods can achieve the best model quality in synchronous FL. However, naively applying momentum in asynchronous FL algorithms leads to slower convergence and degraded model performance. It is still unclear how to effective combinie these two techniques together to achieve a win-win. In this paper, we find that asynchrony introduces implicit bi…

This https://arxiv.org/abs/2307.08672 has been replaced.
link: https://scholar.google.com/scholar?q=a

FedDefender: Backdoor Attack Defense in Federated Learning
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. Our proposed method fingerprints …

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
Kai Li, Xin Yuan, Jingjing Zheng, Wei Ni, Falko Dressler, Abbas Jamalipour
https://arxiv.org/abs/2404.15042

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the be…

CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
Yi Hu, Jinhang Zuo, Alanis Zhao, Bob Iannucci, Carlee Joe-Wong
https://arxiv.org/abs/2403.18451

CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets. Unlike distributed learning frameworks such as federated learning, which often struggle with multimodal data, FMs can transform diverse inputs into embeddings. This process facilitates the integration of information from various modalities and the application of pr…

pfl-research: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, \'Aine Cahill, Rogier van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta Jina, Mona Chitnis
https://arxiv.org/abs/2404.06430 …

pfl-research: simulation framework for accelerating research in Private Federated Learning
Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-u…

Decentralized Federated Unlearning on Blockchain
Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu
https://arxiv.org/abs/2402.16294

Decentralized Federated Unlearning on Blockchain
Blockchained Federated Learning (FL) has been gaining traction for ensuring the integrity and traceability of FL processes. Blockchained FL involves participants training models locally with their data and subsequently publishing the models on the blockchain, forming a Directed Acyclic Graph (DAG)-like inheritance structure that represents the model relationship. However, this particular DAG-based structure presents challenges in updating models with sensitive data, due to the complexity and ov…

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Emre Ozfatura, Kerem Ozfatura, Alptekin Kupcu, Deniz Gunduz
https://arxiv.org/abs/2404.06230

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Federated learning (FL) has been introduced to enable a large number of clients, possibly mobile devices, to collaborate on generating a generalized machine learning model thanks to utilizing a larger number of local samples without sharing to offer certain privacy to collaborating clients. However, due to the participation of a large number of clients, it is often difficult to profile and verify each client, which leads to a security threat that malicious participants may hamper the accuracy o…

Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks
Zahir Alsulaimawi
https://arxiv.org/abs/2403.10005

Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks
The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an innovative security framework inspired by Control-Flow Attestation (CFA) mechanisms, traditionally used in cybersecurity, to ensure software execution integrity. By integrating digital signatures and cryptographic hashing within the FL framework, we authenticate and …

This https://arxiv.org/abs/2302.10084 has been replaced.
link: https://scholar.google.com/scholar?q=a

OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols
Recent secure aggregation protocols enable privacy-preserving federated learning for high-dimensional models among thousands or even millions of participants. Due to the scale of these use cases, however, end-to-end empirical evaluation of these protocols is impossible. We present OLYMPIA, a framework for empirical evaluation of secure protocols via simulation. OLYMPIA. provides an embedded domain-specific language for defining protocols, and a simulation framework for evaluating their performa…