BRIDGE-T: addressing temporal unreliability in federated learning for edge-enabled IoT networks

Federated Learning (FL) in edge-enabled Internet of Things (IoT) networks faces considerable challenges owing to intermittent client participation and distributional drift, and which undermines the stability of a global model's optimization. This coupled impact introduces temporal unreliability, in turn, impairing the training stability. State-of-the-art FL frameworks typically address these challenges in isolation and overlook their coupled impact particularly during the client reintegration process. In order to address this limitation, we propose BRIDGE-T, i.e., a reliability-aware FL framework that addresses temporal unreliability in edge-enabled IoT networks. BRIDGE-T encompasses three components, i.e., (i) Prototype Contrastive Drift Alignment (PCDA) to constrain cross-client representation divergence under evolving non-Independent and Identically Distributed (non-IID) data, (ii) Prototype Query Agreement (PQA) to estimate round-wise clients reliability via cross-client prediction consistency on shared prototypes, and (iii) Reliability-Weighted Asynchronous-aware Aggregation (RWAA) to regulate clients' influence and attenuate stale or misaligned clients' updates. Extensive experiments under varying intermittency and distributional drift on CIFAR-10, CIFAR-100, MNIST, and TON-IoT suggest that BRIDGE-T achieves smoother convergence and greater robustness to client reintegration vis-à-vis the state-of-the-art FL frameworks.