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arXiv Machine Learning By Junyu Lin, Bosong Qi, Pengcheng Shi, Jiayuan Li

Is High Local Accuracy Enough? Geometric Consensus with Support-Adaptive Statistical Personalization for Federated Learning

Read the original on arXiv Machine Learning →

The paper introduces FedGRC, a federated learning framework that enhances both global recognition and personalization for heterogeneous clients. It combines Consensus‑Guided Representation Learning, Multilayer Metric Synthesis, and Support‑Adaptive Shrinkage Personalization to address failures on rare or unseen classes. Experiments on CIFAR‑10, CIFAR‑100, and the FedRS dataset show that FedGRC outperforms 21 baseline methods, improving global accuracy and personalization floors by over 2% in several settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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