Federated Learning

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.

This work identifies federated learning’s communication topology as a privacy channel orthogonal to DP-SGD, and derives a topology-aware noise allocation that provably dominates uniform DP-SGD on the per-client leakage bound at no measurable utility cost.