SPFERE: Towards Practical Semi-Synchronous On-Device Federated Edge Learning With Fairness and Power Awareness

Yixin Chen, Yifan Guo, Wei Yu

Abstract: Federated Edge Learning (FEL) enables privacy-preserving, on-device training across heterogeneous edge devices, reducing data transfer costs. However, most FEL approaches remain simulation-based and fail to address realistic on-device training asynchrony caused by variations in computing power, data volume, and energy availability among devices. To address the issue, we propose SPFERE, a Semi-synchronous Power-aware and FairnEss-Regulated Engine in this paper, designed for power-constrained edge environments and implemented on a real-world edge testbed to support asynchronous model updating, power management, and fairness-aware model aggregation. Specifically, we propose a client grouping-based semi-synchronous aggregation protocol that reduces idle waiting time for power-abundant devices and mitigates stale updates from power-constrained devices, along with our in-depth convergence analysis. Then, we introduce a long short-term memory (LSTM)-based power estimation approach to predict remaining battery voltage for devices with limited communication overhead, enabling early warnings for power dropouts. Lastly, we design fusion-based fairness-aware model aggregation methods to prevent bias by considering device participation frequency and training workload. We systematically validate our framework through experiments on both a simulation platform and a real-device testbed. Our extensive experimental results demonstrate the effectiveness and resilience of SPFERE in dynamic and heterogeneous edge environments.

External IDs:doi:10.1109/tmc.2025.3649621