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What is Clustered Federated Learning for UAVs about?

This paper presents a three-stage Stackelberg game framework for clustered federated learning (CFL) in heterogeneous UAV swarms, addressing challenges related to data heterogeneity and communication inefficiencies. The proposed CFL architecture clusters UAVs based on optimization similarities, enabling efficient resource allocation and privacy-preserving model training without sharing raw data. The performance evaluation demonstrates the effectiveness and stability of the proposed approach, highlighting its

Author

bidk573

Language

EN