The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics

arXiv:2602.01186v2 Announce Type: replace Abstract: Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot federated learning (OFL) alleviates these limitations by reducing communication to a single round, thereby lowering overhead and enhancing practical deployability. Nevertheless, most existing one-shot approaches remain either impractical or constrained, for example, they often depend on the availability of a

Source: arXiv cs.LG — read the full report at the original publisher.

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