Newest papers

Learning to Refer: Inference-Time Collaboration in Decentralized Federated Learning

Yuheng Ma, Chendi Wang, Qiong Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26800 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

分散型連邦化学習では、異質なクライアントは補完的な専門知識を開発する可能性がありますが、各クライアントは通常、その到着地のクライアントによってのみ評価されます。この分散型専門知識をグローバルな連携なしで活用するために、我々は、予算されたローカル・エキスパート・リファレンス、グラフ・リファレンス制限されたルーティングプロセスで、クライアントが隣接するクライアント間のみを移動し、訪問したクライアントのみが最終予測に貢献する場合があります。EdgeDRMは、各クライアントのための密度ベースのルーティング・ポテンシャルを学習することによってこのフレームワークを実装します:隣接的な潜在力間の違いは

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

Abstract

In decentralized federated learning, heterogeneous clients may develop complementary expertise, yet each query is typically evaluated only by the client where it arrives. To exploit this distributed expertise without global coordination, we formulate inference-time collaboration as budgeted local expert referral, a graph-constrained routing process in which a query can move only between neighboring clients and only visited clients contribute to its final prediction. EdgeDRM implements this framework by learning a density-based routing potential for each client: differences between neighboring potentials provide the comparisons used to select successive referrals, while local task models generate predictions that are aggregated along the resulting route. Both training and inference are decentralized, using only neighbor-local information without a central router or graph-wide client scoring. We establish estimation guarantees for the learned comparisons and characterize how local improvement and graph structure govern prior-weighted density gains from referral. Experiments on benchmark and real-world medical data show that EdgeDRM outperforms graph-matched decentralized baselines in most settings, with the clearest benefits under sparse connectivity.

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Topics: Computer science, Exploit, Inference, Task (project management), Process (computing), Benchmark (surveying)

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