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CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning
Conventional federated learning relies on parameter averaging, which forces clients to be doubly homogeneous: all must run an identical architecture, and accuracy degrades when local data are non-IID. Decentralized federated distillation sidesteps both: each client runs its peers' model snapshots as
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