Article

On the Interaction Between Personalization and Optimization in Federated Learning for Medical Image Classification

2026-06-03

Abstract excerpt

<title>Abstract</title> <p>Federated learning (FL) has emerged as a promising paradigm for privacy-preserving medical image analysis, enabling collaborative model training across distributed institutions without sharing sensitive patient data. However, two key challenges remain: instability of model optimization under non-independent and identically distributed(non-IID)data, and the need for client-specific adapt...

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Literature Corpus work: c42ca77e-5056-513a-b64a-674a7021a031
DOI: 10.21203/rs.3.rs-9888411/v1

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