BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

6 February 2022

Papers citing "BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning"

7 / 7 papers shown

Title
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning Ehsan Hallaji R. Razavi-Far M. Saif 46 0 0 23 Feb 2025
Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning Joon Kim Sejin Park AAML FedML 40 1 0 15 Aug 2024
Decentralized Federated Learning: A Survey on Security and Privacy Ehsan Hallaji R. Razavi-Far M. Saif Boyu Wang Qiang Yang FedML 58 34 0 25 Jan 2024
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks Jorge Castillo Phillip Rieger Hossein Fereidooni Qian Chen Ahmad Sadeghi FedML AAML 38 8 0 03 Oct 2023
Privacy-Preserving Aggregation in Federated Learning: A Survey Ziyao Liu Jiale Guo Wenzhuo Yang Jiani Fan Kwok-Yan Lam Jun Zhao FedML 32 87 0 31 Mar 2022
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing Dinh C. Nguyen Seyyedali Hosseinalipour David J. Love P. Pathirana Christopher G. Brinton 31 47 0 18 Mar 2022
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 358 0 24 Mar 2020