用区块链实现去中心化模型更新检测，防联邦学习中毒攻击
FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
- 各客户端共同生成并验证一个判别模型，达成共识
- 实验表明系统能有效抵御多种数据投毒攻击
- 无需依赖可信中心，适合隐私敏感的物联网场景
联邦学习通过利用物联网设备的私有数据联合训练模型，提升传统深度学习的隐私保护能力，但在训练过程中易受数据投毒攻击影响，导致模型性能与完整性下降。现有中毒检测方法缺乏统一标准或过度依赖信任机制。本文提出 \\_Sys，一种基于区块链的联邦学习中毒检测框架，将全局服务器角色去中心化至各参与客户端。每个客户端生成并验证一个判别模型，通过共识机制形成唯一判别模型。实现结果表明，\\Sys 能有效抵御各类数据投毒攻击，且判别模型生成过程具有可扩展性。
原文摘要 · Abstract (English)
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.
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