2XvF67vbCK@OpenReview

#1 Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning [PDF¹] [Copy] [Kimi] [REL]

Authors: Hui Zeng, Wenke Huang, Tongqing Zhou, Xinyi Wu, Guancheng Wan, Yingwen Chen, CAI ZHIPING

Turning the multi-round vanilla Federated Learning into one-shot FL (OFL) significantly reduces the communication burden and makes a big leap toward practical deployment. However, this work empirically and theoretically unravels that existing OFL falls into a garbage (inconsistent one-shot local models) in and garbage (degraded global model) out pitfall. The inconsistency manifests as divergent feature representations and sample predictions. This work presents a novel OFL framework FAFI that enhances the one-shot training on the client side to essentially overcome inferior local uploading. Specifically, unsupervised feature alignment and category-wise prototype learning are adopted for clients' local training to be consistent in representing local samples. On this basis, FAFI uses informativeness-aware feature fusion and prototype aggregation for global inference. Extensive experiments on three datasets demonstrate the effectiveness of FAFI, which facilitates superior performance compared with 11 OFL baselines (+10.86% accuracy). Code available at https://github.com/zenghui9977/FAFI_ICML25

Subject: ICML.2025 - Poster

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