Gradient-Aware Participation for Energy Reduction in Federated Learning with Extreme Label Skew

Federated Learning (FL) enables distributed clients to train a global classification model collaboratively while preserving data privacy. A major challenge in FL is ensuring efficient training with limited computing and communication resources, especially when clients’ datasets contain samples from a restricted subset of target classes, a problem known as extreme label skew. Under such a condition, model updates from clients are biased toward their local data distributions, resulting in slow convergence and increased energy consumption due to the need for additional training rounds. This paper introduces FL with Gradient-Aware Participation (FedGAP), a novel strategy aimed at reducing energy consumption while preserving model accuracy even with extreme label skew. FedGAP dynamically adjusts the cohort size, i.e., the number of participating clients per training round, based on the evolution of the global model’s pseudo-gradient. By detecting stagnant phases where progress toward convergence stalls, FedGAP increases the cohort size to escape suboptimal regions and accelerate learning, thereby minimizing the waste of resources. Experiments on CIFAR-10 and CIFAR-100 demonstrate that FedGAP achieves up to 2.74× greater energy efficiency compared to state-of-the-art methods without compromising accuracy.