Privacy-preserving model splitting and quality-aware device association for federated edge learning

Abstract

Federated edge learning (FEEL) provides a promising device-edge collaborative learning paradigm, which enables edge devices to parallel participate in model co-creation while preserving user privacy, opening countless opportunities to enable edge intelligence. With the growing demand for intelligent services, extensive FEEL deployment is inevitable. Nevertheless, existing FL schemes neglect two unique features (i.e., resource heterogeneity and data heterogeneity) in real-world edge learning and thus may negatively affect the training efficiency and accuracy. Specifically, (1) heterogeneous and limited device resources cause massive laggards, which bring intolerable training delay; (2) heterogeneous data distribution causes device quality divergence, bringing severe training accuracy degradation. This article proposes a split-based FEEL framework and an adaptive model splitting and quality-aware device association scheme (MSDA) to tackle the aforementioned challenges. MSDA contains two levels: at the model splitting level, according to device capability and model structure, an adaptive splitting mechanism is proposed to provide a low-latency and privacy-preserving model splitting strategy for each device and guide subsequent device association. At the device association level, each device is simulated as a player with a quality weight in the potential game. Then a quality-aware decentralized device association mechanism is designed to ensure that more high-quality devices upload local updates before the deadline with the help of the edge server. Finally, experimental results demonstrate that MSDA yields significant improvements, achieving up to 3.1 (Formula presented.) training speedup and 39% accuracy improvement compared to state-of-the-art methods.