Federated Learning with Reliability-Aware Workload Allocation in Distributed Edge Computing

Federated Learning (FL) enables collaborative model training across various distributed devices without sharing raw data. Client failures, variable energy availability, and outages of edge servers contribute to unreliable training participation, incomplete model updates, and failures at the system level during the aggregation process. In this study, we introduce a reliability-aware workload allocation FL framework (FedRAW) aimed at improving system reliability in failure-prone edge computing systems. Our approach dynamically modifies client workloads based on their failure history and integrates a lightweight backup mechanism to maintain aggregation continuity during edge server failures by backup servers handling. Additionally, we employ Bayesian optimization to fine-tune workload parameters, achieving improved energy efficiency. Experimental results reveal that our proposed method improves model accuracy while reducing energy consumption compared to recent federated learning algorithms.