  Due to the curse of statistical heterogeneity across clients, adopting a
personalized federated learning method has become an essential choice for the
successful deployment of federated learning-based services. Among diverse
branches of personalization techniques, a model mixture-based personalization
method is preferred as each client has their own personalized model as a result
of federated learning. It usually requires a local model and a federated model,
but this approach is either limited to partial parameter exchange or requires
additional local updates, each of which is helpless to novel clients and
burdensome to the client's computational capacity. As the existence of a
connected subspace containing diverse low-loss solutions between two or more
independent deep networks has been discovered, we combined this interesting
property with the model mixture-based personalized federated learning method
for improved performance of personalization. We proposed SuPerFed, a
personalized federated learning method that induces an explicit connection
between the optima of the local and the federated model in weight space for
boosting each other. Through extensive experiments on several benchmark
datasets, we demonstrated that our method achieves consistent gains in both
personalization performance and robustness to problematic scenarios possible in
realistic services.
