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InfoResearchPeer-reviewed
FedMPS: Federated Learning in a Synergy of Multi-Level Prototype-Based Contrastive Learning and Soft Label Generation
- Published
- Record updated
Summary
FedMPS is a federated learning framework that combines multi-level prototype-based contrastive learning with prototype-guided soft label generation. Instead of exchanging model parameters, clients transmit only prototypes and soft labels, which the authors report reduces global knowledge shift and communication costs. Experiments on six publicly available datasets compare it against current state-of-the-art federated learning approaches.