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cs.LG2026
CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning
Yifan Ying, Qing Tian
Conventional federated learning relies on parameter averaging, which forces clients to be doubly homogeneous: all must run an identical architecture, and accuracy degrades when loc…
cs.LG2026
CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning
Baraa Bilbeisi, Mengchen Fan, Baocheng Geng +1
Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architecture and degrades under non-IID d…
cs.LG2025
PFedDST: Personalized Federated Learning with Decentralized Selection Training
Mengchen Fan, Keren Li, Tianyun Zhang +2
Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability dis…