Ajay Kumar Shrestha, Sandhya Joshi · 2026 · arXiv
DOI: 10.48550/arXiv.2610.25714 · open-access PDF
信頼と評判のメカニズムは、連邦学習(FL)における悪意のあるクライアントの影響を減らすことができますが、非独立性と同一分散性(non-IID)データでは厳格な更新フィルタリングが困難であるため、誠実なローカルアップデートは、共有認証プロキシでのパフォーマンスを一時的に低下させることがあります。信頼に基づいた以前の半分散型FL(SDFL)フレームワークに基づき、この論文は信頼信号を操作化し、強力な認証ユーティリティスコアと方向と基準に基づく更新品質を組み合わせた peer-relative update screening を導入します。
Machine translation (Meta M2M100); the English original follows.
Trust and reputation mechanisms can reduce the influence of malicious clients in federated learning (FL), but strict update filtering is difficult under non-independent and identically distributed (non-IID) data because honest local updates may temporarily reduce performance on a shared validation proxy. Building on our earlier trust-based semi-decentralized FL (SDFL) framework, this paper operationalizes the trust signals and introduces peer-relative update screening that combines a robust validation-utility score with direction- and norm-based update quality. The method is evaluated with 50
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.25714, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange