Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
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.