  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.
