为去中心化联邦学习设计可审计的聚合节点验证框架，防止单点作恶。
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
- 用历史行为评分筛选聚合节点，结合区块链存证。
- 通过HSIC检测客户端更新与模型间的统计独立性异常。
- 适用于高安全需求的分布式机器学习场景。
去中心化联邦学习（DFL）因无中心服务器，需在每轮中指定特定参与者负责模型聚合。现有架构虽确保聚合节点选任时的可信性，却未考虑其当选后可能恶意作乱。本文提出名为TrustChain的DFL框架，通过分析节点过往行为进行评分，并在聚合后实施审计。该方法利用希尔伯特-施密特独立性准则（HSIC）持续监控客户端更新与聚合模型之间的统计独立性。系统融合区块链、异常检测与概念漂移分析等技术，在多个联邦数据集及不同拜占庭节点数量的攻击场景下进行了评估。
原文摘要 · Abstract (English)
The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architectures ensure the trustworthiness of the aggregator node upon selection. However, most of these studies overlook the possibility that the aggregating node may turn rogue and act maliciously after being nominated. To address this problem, this paper proposes a DFL structure, called TrustChain, that scores the aggregators before selection based on their past behavior and additionally audits them after the aggregation. To do this, the statistical independence between the client updates and the aggregated model is continuously monitored using the Hilbert-Schmidt Independence Criterion (HSIC). The proposed method relies on several principles, including blockchain, anomaly detection, and concept drift analysis. The designed structure is evaluated on several federated datasets and attack scenarios with different numbers of Byzantine nodes.
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