HARMONY mitigates representation skew in heterogeneous hybrid split federated learning via meta-learning to simulate diverse extractors and server-side contrastive learning to align features, delivering up to 43% accuracy gains.
Ensemble distillation for robust model fusion in federated learning.Proceedings of Advances in Neural Information Processing Systems, 33:2351–2363
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HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
HARMONY mitigates representation skew in heterogeneous hybrid split federated learning via meta-learning to simulate diverse extractors and server-side contrastive learning to align features, delivering up to 43% accuracy gains.