SketchGuard makes Byzantine-robust decentralized federated learning communication-efficient by screening neighbors on compact sketches, with three rules that provably close the attacks sketching opens, matching the accuracy of the defenses it wraps while cutting bytes by up to 41% and emulated edge-cloud round time by 39%
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 paper presents Murmura, a DFL framework for wearable IoT that uses evidential deep learning for trust-aware model personalization. Epistemic uncertainty indicates peer compatibility, enabling nodes to exclude incompatible peers. Evaluation on three wearable datasets shows only 0.9% performance degradation under non-IID conditions.