Federated Learning

This work identifies hierarchical federated learning’s aggregation topology as a privacy channel orthogonal to DP-SGD, and derives a topology-aware noise allocation that provably matches uniform DP-SGD’s per-client privacy guarantee while recovering up to 88% of the noise budget.

This chapter presents a comprehensive taxonomy of blockchain-enabled federated learning (BCFL) systems, analyzing coordination structures, consensus mechanisms, storage architectures, and trust models to demonstrate how specialized protocols can enable trustless collaborative machine learning across diverse domains.