Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized model when collaborating with others. A key question in PFL is to decide which parameters of a client should be localized or shared with others. In current mainstream approaches, all layers that are sensitive to non-IID data (such as classifier layers) are generally personalized. The reasoning behind this approach is understandable, as localizing parameters that are easily influenced by non-IID data can prevent the potential negative effect of collaboration. However, we believe that this approach is too conservative for collaboration. For example, for a certain client, even if its parameters are easily influenced by non-IID data, it can still benefit by sharing these parameters with clients having similar data distribution. This observation emphasizes the importance of considering not only the sensitivity to non-IID data but also the similarity of data distribution when determining which parameters should be localized in PFL. This paper introduces a novel guideline for client collaboration in PFL. Unlike existing approaches that prohibit all collaboration of sensitive parameters, our guideline allows clients to share more parameters with others, leading to improved model performance. Additionally, we propose a new PFL method named FedCAC, which employs a quantitative metric to evaluate each parameter's sensitivity to non-IID data and carefully selects collaborators based on this evaluation. Experimental results demonstrate that FedCAC enables clients to share more parameters with others, resulting in superior performance compared to state-of-the-art methods, particularly in scenarios where clients have diverse distributions.

翻译：个性化联邦学习（PFL）通过允许每个客户端在与其他客户端协作时训练个性化模型，减少了非独立同分布（non-IID）数据对客户端的影响。PFL中的一个关键问题是决定客户端的哪些参数应本地化或与其他客户端共享。在当前主流方法中，所有对非IID数据敏感的层（如分类器层）通常被个性化。这种方法的理由可以理解，因为本地化易受非IID数据影响的参数可以防止协作可能带来的负面效果。然而，我们认为这种方法对协作过于保守。例如，对于某个客户端，即使其参数易受非IID数据影响，它仍然可以通过与具有相似数据分布的客户端共享这些参数而受益。这一观察强调了在确定PFL中哪些参数应本地化时，不仅需要考虑对非IID数据的敏感性，还需考虑数据分布的相似性。本文提出了一种PFL客户端协作的新准则。与现有禁止所有敏感参数协作的方法不同，我们的准则允许客户端与其他客户端共享更多参数，从而提升模型性能。此外，我们提出了一种名为FedCAC的新PFL方法，该方法采用定量指标评估每个参数对非IID数据的敏感性，并基于此评估谨慎选择协作伙伴。实验结果表明，FedCAC使客户端能够与其他客户端共享更多参数，在客户端分布多样化的场景中，其性能优于最先进的方法。