Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy. The heterogeneity of devices and networking resources of the participants delay the training and aggregation. The paper introduces a novel approach to federated learning by incorporating resource-aware clustering. This method addresses the challenges posed by the diverse devices and networking resources among participants. Unlike static clustering approaches, this paper proposes a dynamic method to determine the optimal number of clusters using Dunn Indices. It enables adaptability to the varying heterogeneity levels among participants, ensuring a responsive and customized approach to clustering. Next, the paper goes beyond empirical observations by providing a mathematical derivation of the communication rounds for convergence within each cluster. Further, the participant assignment mechanism adds a layer of sophistication and ensures that devices and networking resources are allocated optimally. Afterwards, we incorporate a master-slave technique, particularly through knowledge distillation, which improves the performance of lightweight models within clusters. Finally, experiments are conducted to validate the approach and to compare it with state-of-the-art. The results demonstrated an accuracy improvement of over 3% compared to its closest competitor and a reduction in communication rounds of around 10%.
This is implementation of Fed-RAC on HAR dataset and can be modified for other datasets.
Paper Link: Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning