This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses trust challenges in distributed computing through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol.
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 proposes SketchGuard, a novel framework that scales Byzantine-robust decentralized federated learning through sketch-based compression, achieving up to 82% reduction in computation time and 50-70% communication savings while maintaining identical robustness to state-of-the-art methods.
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.
This paper proposes a multi-layer IoT-Edge-Cloud healthcare architecture combining differential privacy with a hybrid Laplace-Gaussian noise mechanism, achieving 82-84% ML accuracy while reducing inference attacks by up to 70%, with blockchain for data integrity and edge computing delivering 8× latency reduction for emergencies.
This paper identifies network topology as a fundamental privacy vulnerability in federated learning systems, demonstrating that adversaries can infer sensitive data distribution patterns through communication patterns, parameter magnitudes, and structural correlations even under strong differential privacy guarantees.