SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning
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 sta...
| Authors | Ke Xiao, Qiyuan Wang, Christos Anagnostopoulos |
|---|---|
| Primary category | cs.DC |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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