Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization. (arXiv:2211.05554v2 [cs.LG] UPDATED)

Non-IID data distribution across clients and poisoning attacks are two main
challenges in real-world federated learning (FL) systems. While both of them
have attracted great research interest with specific strategies developed, no
known solution manages to address them in a unified framework. To universally
overcome both challenges, we propose SmartFL, a generic approach that optimizes
the server-side aggregation process with a small amount of proxy data collected
by the service provider itself via a subspace training technique. Specifically,
the aggregation …

aggregation arxiv data federated learning optimization