FedAGF: Adaptive Concurrency via Gradient Feedback for Mitigating Extreme Label Skew in Budget-Constrained Federated Learning

Cross-device Federated Learning (FL) enables large fleets of distributed edge devices to collaboratively train a global classification model without sharing their data. The resulting training quality is strongly limited by extreme label skew, a condition where each device holds samples from a subset of the target classes. In such cases, local updates become biased toward local distributions, slowing down convergence and degrading the global model accuracy. These effects get critical when devices operate under constrained energy budgets that restrict their participation to a limited number of synchronization rounds, further reducing the achievable accuracy. To overcome these limitations, we introduce Federated Learning with Adaptive Concurrency via Gradient Feedback (FedAGF), a control policy that dynamically adjusts the number of devices selected for synchronization throughout training. FedAGF adapts to training dynamics by monitoring global model updates and elevating participation whenever progress slows. This adaptive mechanism balances accuracy improvement with efficient use of devices’ energy budgets, allocating resources when they provide the greatest benefit to convergence. Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate that FedAGF effectively mitigates extreme label skew, achieving up to 14.14% higher accuracy than state-of-the-art FL methods and enabling efficient and scalable training, even under skewed label distributions and resource constraints.