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Discount Yourself: Self-inclusive Freshness-weighted Gossip for Decentralised Learning under Client Outages

Qiyuan Wang, Ke Xiao, Christos Anagnostopoulos · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28545 · open-access PDF

Chinese

去中心化联邦学习(DFL)在与他们的邻居而不是中央服务器平均的设备上训练个性化模型. 然而,真正的设备往往离线数小时或数天,而客户端从中断中回来只能通过汇总其邻居拥有的模型来恢复。 我们观察到,这些模型中最稳定的通常是客户端的模型,这种情况是现有的停滞意识规则,这些规则只减少客户端接收的模型,而不是解决。

Machine translation (Meta M2M100); the English original follows.

Abstract

Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or days, and a client returning from an outage can recover only by aggregating the models its neighbourhood holds. We observe that the stalest of these models is usually the client's own, a case that existing staleness-aware rules, which discount only the models a client receives, do not address. We formalise aggregation after an outage as estimating the state of a fully available network and derive

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