Tootfinder

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…

5G-WAVE: A Core Network Framework with Decentralized Authorization for Network Slices
Pragya Sharma, Tolga Atalay, Hans-Andrew Gibbs, Dragoslav Stojadinovic, Angelos Stavrou, Haining Wang
https://arxiv.org/abs/2404.13242

5G-WAVE: A Core Network Framework with Decentralized Authorization for Network Slices
5G mobile networks leverage Network Function Virtualization (NFV) to offer services in the form of network slices. Each network slice is a logically isolated fragment constructed by service chaining a set of Virtual Network Functions (VNFs). The Network Repository Function (NRF) acts as a central OpenAuthorization (OAuth) 2.0 server to secure inter-VNF communications resulting in a single point of failure. Thus, we propose 5G-WAVE, a decentralized authorization framework for the 5G core by leve…

Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Hongbin Liu, Michael K. Reiter, Neil Zhenqiang Gong
https://arxiv.org/abs/2402.14977 htt…

Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Foundation model has become the backbone of the AI ecosystem. In particular, a foundation model can be used as a general-purpose feature extractor to build various downstream classifiers. However, foundation models are vulnerable to backdoor attacks and a backdoored foundation model is a single-point-of-failure of the AI ecosystem, e.g., multiple downstream classifiers inherit the backdoor vulnerabilities simultaneously. In this work, we propose Mudjacking, the first method to patch foundation …

Performance Analysis of Decentralized Physical Infrastructure Networks and Centralized Clouds
Jan von der Assen, Christian Killer, Alessandro De Carli, Burkhard Stiller
https://arxiv.org/abs/2404.08306

Performance Analysis of Decentralized Physical Infrastructure Networks and Centralized Clouds
The advent of Decentralized Physical Infrastructure Networks (DePIN) represents a shift in the digital infrastructure of today's Internet. While Centralized Service Providers (CSP) monopolize cloud computing, DePINs aim to enhance data sovereignty and confidentiality and increase resilience against a single point of failure. Due to the novelty of the emerging field of DePIN, this work focuses on the potential of DePINs to disrupt traditional centralized architectures by taking advantage of the …

This https://arxiv.org/abs/2312.04135 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices.However, these approaches face challenges including computation and storage costs, along with a single point of failure risk, threatening data privacy and availabilit…

This https://arxiv.org/abs/2310.07079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Secure Decentralized Learning with Blockchain
Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been considered an attractive solution for machine learning tasks on distributed personal devices. However, this process is vulnerable to attacker…