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arXiv Machine Learning By Yifan Ying, Qing Tian

CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning

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CALM introduces a smooth trust gating mechanism for decentralized federated learning, replacing hard filtering of teacher models with class‑wise, sample‑wise, and label‑based weighting. It allows clients with heterogeneous architectures to distill knowledge from peers without a central server or shared data, even under severe non‑IID label skew. Experiments on CIFAR‑10, SVHN, OrganAMNIST, and Google Speech Commands show that CALM consistently outperforms uniform and hard‑filtered distillation and matches or exceeds other heterogeneous‑FL methods.

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