通过分层信任网络提升联邦学习抗恶意攻击能力，尤其适合数据异构场景。
FoggyTrust: Robust Federated Learning with Hierarchical Trust Networks
- 将信任计算下沉至边缘节点，实现本地信任评估与全局聚合分离。
- 在CIFAR-10上对齐攻击下相比FLTrust性能提升超50%。
- 适用于野生动物监测等高安全要求的真实场景。
拜占庭鲁棒联邦学习旨在保护分布式模型训练免受恶意或损坏客户端的影响，且无需访问其私有数据。FLTrust通过引入服务器端可信根数据集，为客户端更新分配信任分数以实现更鲁棒的聚合。本文提出FOGGYTRUST，作为FLTrust的分层扩展，将信任计算局部化至雾节点，使框架能更好处理全局数据异构性，同时在局部同质客户端组中保持鲁棒性。我们进一步表明，该两级架构可通过结合基于本地信任的聚合与异构感知全局优化器（如FedAdam和SCAFFOLD），同时缓解信任估计中的分布偏差和跨组客户端漂移问题。在基准数据集上，FOGGYTRUST在更具挑战性的异构设置中表现最佳，尤其在CIFAR-10上的Krum和Trim攻击下，性能较FLTrust提升超过50%。我们还在真实世界猎豹数据集上测试了FOGGYTRUST，验证了分层信任网络在分布式野生动物监测等社会影响大、安全关键场景中的潜力。
原文摘要 · Abstract (English)
Byzantine-robust federated learning seeks to protect distributed model training from malicious or corrupted clients without requiring access to their private data. FLTrust addresses this challenge by introducing a trusted server-side root dataset that assigns trust scores to client updates for more robust aggregation. In this work, we propose FOGGYTRUST, a hierarchical extension of FLTrust that localizes trust computation to fog nodes, allowing the framework to better handle globally heterogeneous data while preserving robustness within locally homogeneous client groups. We further show that this two-level architecture can simultaneously address distribution mismatch in trust estimation and client drift across groups by combining local trust-based aggregation with heterogeneity-aware global optimizers such as FedAdam and SCAFFOLD. Across benchmark datasets, FOGGYTRUST achieves its strongest gains on more challenging heterogeneous settings, particularly on CIFAR-10 under Krum and Trim attacks, where it achieves an over 50% improvement over FLTrust. We also test FOGGYTRUST in a real-world safari dataset to show the promise of hierarchical trust networks for robust federated learning in socially impactful, safety-critical settings such as distributed wildlife monitoring.
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