Tootfinder

👀 Forgejo's "Roadmap for Federation" https://codeberg.org/forgejo-contrib/federation/src/branch/main/FederationRoadmap.md — still looks like a lot of work need to be done 🥲

federation/FederationRoadmap.md at main
federation

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li, Yinlin Zhu, Meixia Qu, Wenyu Wang
https://arxiv.org/abs/2508.11530

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predomi…

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Ratun Rahman, Atit Pokharel, Dinh C. Nguyen
https://arxiv.org/abs/2507.12492

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQ…

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Nina Cai, Jinguang Han
https://arxiv.org/abs/2506.12846

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malici…

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Jundong Chen, Honglei Zhang, Chunxu Zhang, Fangyuan Luo, Yidong Li
https://arxiv.org/abs/2508.12386

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personalization, leading to suboptimal performance, which we term the aggregation bottleneck. This issue stems from the inherent heterogeneity across numerous clients in FR, which drives the globally aggregated…

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Haolong Jin, Shenglin Liu, Cong Cong, Qingmin Feng, Yongzhi Liu, Lina Huang, Yingzi Hu
https://arxiv.org/abs/2506.15365

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserving sensitive patient data. In the context of Whole Slide Image (WSI) classification, FL faces significant challenges, including heterogeneous computational resources across participating medical institutes and privacy concerns. To address these challenges, we propose FedWSIDD, a novel FL paradigm that leverages datas…

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…

SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Ratun Rahman, Atit Pokharel, Md Raihan Uddin, Dinh C. Nguyen
https://arxiv.org/abs/2508.12477 https:/…

SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine learning (ML) environment. However, the majority of available quantum simulators are primarily built for general quantum circuit simulation and do not include integrated support for machine learning tasks such as training, evaluation, and iterative optimization. Furthermore, designing and assessing quantum learning alg…

Safeguarding Federated Learning-based Road Condition Classification
Sheng Liu, Panos Papadimitratos
https://arxiv.org/abs/2507.12568 https://

Safeguarding Federated Learning-based Road Condition Classification
Federated Learning (FL) has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification (RCC) systems, harnessing distributed sensing, computing, and communication resources on board vehicles without sharing sensitive image data. However, the collaborative nature of FL-RCC frameworks introduces new vulnerabilities: Targeted Label Flipping Attacks (TLFAs), in which malicious clients (vehicles) deliberately alter their trainin…

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han, Heqing Ren, Xi Cheng, Zien Zeng, Lucas Krauter, Gerome Bovet, Burkhard Stiller
https://arxiv.org/abs/2507.13313

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
This paper introduces a dataset and experimental study for decentralized federated learning (DFL) applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls, file system activities, resource usage, kernel events, input/output events, and network records. These records were aggregated into 30-second windows, resulting in 342,106 features used for model trai…