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Federated Learning is a training framework that enables multiple participantsto collaboratively train a shared model while preserving data privacy andminimizing communication overhead. The heterogeneity of devices and networkingresources of the participants delay the training and aggregation in federatedlearning. This paper proposes a federated learning approach to manoeuvre theheterogeneity among the participants using resource aware clustering. Theapproach begins with the server gathering information about the devices andnetworking resources of participants, after which resource aware clustering isperformed to determine the optimal number of clusters using Dunn Indices. Themechanism of participant assignment is then introduced, and the expression ofcommunication rounds required for model convergence in each cluster ismathematically derived. Furthermore, a master-slave technique is introduced toimprove the performance of the lightweight models in the clusters usingknowledge distillation. Finally, experimental evaluations are conducted toverify the feasibility and effectiveness of the approach and to compare it withstate-of-the-art techniques.

Resource Aware Clustering for Tackling the Heterogeneity of Participants in Federated Learning