针对医疗影像分割，提出按客户端自适应的个性化联邦学习方法
Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation
- 用残差费雪信息矩阵衡量参数对领域差异的敏感度
- 通过模拟数据估计隐私保护下的参数敏感度，实现精准分区
- 仅聚合不变参数，提升各机构模型性能，适合医疗数据协作
联邦学习使多个客户端（医疗机构）在不共享私有数据的情况下协同训练模型。为应对客户端间的数据异质性问题，个性化联邦学习（pFL）旨在为每个客户端学习定制化模型。本文提出pFL-ResFIM框架，实现参数层面的客户端自适应个性化。具体地，引入新的度量指标——残差费雪信息矩阵（ResFIM），用于量化模型参数对领域差异的敏感度。在隐私约束下，采用谱迁移策略生成反映不同客户端领域风格的模拟数据，以估计各客户端的ResFIM。基于估计结果，将模型参数划分为领域敏感与领域无关两部分。服务器仅聚合领域无关参数，构建各客户端的个性化模型。在公开数据集上的大量实验表明，pFL-ResFIM持续优于现有先进方法，验证了其有效性。
原文摘要 · Abstract (English)
Federated learning enables multiple clients (institutions) to collaboratively train machine learning models without sharing their private data. To address the challenge of data heterogeneity across clients, personalized federated learning (pFL) aims to learn customized models for each client. In this work, we propose pFL-ResFIM, a novel pFL framework that achieves client-adaptive personalization at the parameter level. Specifically, we introduce a new metric, Residual Fisher Information Matrix (ResFIM), to quantify the sensitivity of model parameters to domain discrepancies. To estimate ResFIM for each client model under privacy constraints, we employ a spectral transfer strategy that generates simulated data reflecting the domain styles of different clients. Based on the estimated ResFIM, we partition model parameters into domain-sensitive and domain-invariant components. A personalized model for each client is then constructed by aggregating only the domain-invariant parameters on the server. Extensive experiments on public datasets demonstrate that pFL-ResFIM consistently outperforms state-of-the-art methods, validating its effectiveness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。