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arXiv AI By Quan Minh Nguyen, Hoang M. Ngo, Bhakti Chowkwale, Yasaman Keshtkarjahromi, My T. Thai

FedFiOT: Fisher-Weighted Optimal Transport for Model Merging in One-Shot and Few-Round Federated Learning

Read the original on arXiv AI →

FedFiOT introduces a Fisher-weighted optimal transport framework for merging client models in one-shot and few-round federated learning. By using Fisher information to assess parameter importance, it aligns mismatched neurons and weights parameters during aggregation. The method alternates between optimizing neuron correspondences and global model parameters, and theoretical analysis shows monotonic improvement of the merging objective with a bound on heterogeneity effects. Experiments on multiple datasets confirm consistent performance gains over existing baselines.

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 AI.

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