Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
Hongrui Shi, Valentin Radu, Po Yang · 2025
With the rapid proliferation of edge devices, such as those in the Internet of Things (IoT), which generate critical data for machine learning applications, it is essential to enable their participation in privacy-preserving Federated Learning (FL) systems. Given their limited computational resources, an effective approach is to adapt and reduce their training workload to align with their capabilities. Previous FL research has focused primarily on workload reduction through lightweight models at the edge, with limited attention given to optimizing on-device training efficiency by reducing the amount of data required for training. In this work, we propose FedFT-EDS, a novel approach that combines Fine-Tuning of partial client models with Entropy-based Data Selection to reduce training workloads on edge devices. By actively selecting the most informative local instances for learning, FedFT-EDS significantly reduces the training data in FL and demonstrates that not all user data are equally beneficial across training rounds. We show that FedFT-EDS uses only 50% of the available training data while improving the global model performance compared to the baseline methods, FedAvg and FedProx. Importantly, FedFT-EDS improves the learning efficiency of client models by up to 3×, to achieve a similar performance to the baselines in only one third of their training time. This work underscores the critical role of data selection in Federated Learning and offers a promising direction for achieving scalable and efficient FL systems.