SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning

2026-09-20

AI Summary

本文提出SAGE方法解决去中心化联邦学习中的最优停止同伴选择问题，通过基于锚点证据的评分机制决定是否继续探查或随机选择。

Abstract

Decentralised federated learning replaces server aggregation with peer-to-peer model exchange, making collaborator selection a local decision under uncertainty. Fixed probe budgets waste effort on easy choices yet fall short when peers are hard to distinguish. We propose SAGE (Sequential Anchor-Gated Exchange), an optimal-stopping peer selector under a one-model-bearing-exchange budget. A receiver scores candidate neighbours on receiver-owned anchor evidence and selects once an advantage is certified. It continues probing only while further evidence repays its cost, and otherwise falls back to random gossip. We show that the stopping problem admits an optimal rule attained at a finite stage, and that the anchor schedule is order-optimal in the peer-risk gap and the confidence level. We further show that the selector never returns a peer worse than random gossip with high probability, and prove that no such guarantee holds for selectors that commit without a certificate. A separability threshold follows, below which no probing budget improves on gossip. Experiments span two image benchmarks, two graph families and three heterogeneity levels. Selectors that always act on their evidence lose to gossip in every configuration tested. SAGE-OS matches gossip on 75.5% less evidence than a fixed budget, at half the communication overhead of two published selectors. The operative decision is not which peer to rank first, but whether the evidence justifies ranking at all.

Problem

• Decentralised Federated Learning
• Peer Selection
• Optimal Stopping
• Uncertainty
• Probe Budget

Innovation

• Optimal Stopping
• Decentralised Federated Learning
• Peer Selection
• Anchor Evidence
• Communication Overhead