计算机科学
Byzantine容错
服务器
差别隐私
云计算
信息隐私
块链
加密
人工智能
可追溯性
分布式计算
计算机安全
容错
机器学习
计算机网络
数据挖掘
软件工程
操作系统
作者
Minghui Xu,Zongrui Zou,Ye Cheng,Qin Hu,Dongxiao Yu,Xiuzhen Cheng
标识
DOI:10.1109/tc.2022.3169436
摘要
Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system? To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.
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