用区块链防联邦学习中的模型投毒和服务器作弊
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
- 基于智能合约的区块链架构，避免单点故障
- 在真实数据集上验证，对抗后门攻击效果优于同类方法
- 适合对安全要求高的分布式学习场景
联邦学习（FL）是一种在分布式设备上本地训练模型、无需集中数据的机器学习方法。然而，其面临严重安全风险：一是依赖中心服务器聚合模型，存在单点故障，服务器可能恶意操作；二是客户端可发动后门攻击，在提交模型前注入恶意代码，导致整体性能下降。为此，本文提出FedBlock，一种基于区块链的新型联邦学习框架，通过仅使用智能合约实现，可部署于任意区块链网络。该框架在真实数据集上经过全面评估，对抗后门攻击的能力与现有文献相当，且能同时解决服务器端风险，而现有防御方法多未覆盖此问题。
原文摘要 · Abstract (English)
Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do.
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