Parameterized Knowledge Transfer for Personalized Federated Learning

the main idea of this paper is to allow each client to maintain a personalized soft prediction at the server that can be updated by a linear combination of all clients’ local soft predictions using a knowledge coefficient matrix in order to reinforce the collaboration between clients with similar data distributions. the knowledge coefficient matrix is parameterized so that it can be trained.