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FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification

Paper recorded by Signals 4 on 2026-09-04 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.LG · 机器学习 · first seen 2026-09-07

Abstract

Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed across hospitals and cannot be centralized to protect patient privacy. Federated Learning (FL) addresses this, since hospitals train one shared diagnostic model while patient data remain local. Training proceeds in communication rounds, in which eac

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#124 most recent of 215 cs.LG papers we have recorded · ↑ newer: Hessian-based molecular conformation augmentation for a scalable and e · ↓ older: A Verifier-Guided Explainable Reasoning Framework with Gold-Anchored Q
Cite this page: FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification: the #124 most recent of 215 cs.LG papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/feddraw-federated-dual-reputation-annealing-weighting-for-heterogeneous-multi-in.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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