The Diversity Bonus: Learning From Dissimilar Clients in Personalized Federated Learning.

Abstract

Personalized federated learning (PFL) allows clients to collaboratively train their personalized models to handle situations where data from different clients are not independent and identically distributed (non-IID). Previous PFL research implicitly assumes that clients benefit most from those with similar data distributions. Correspondingly, methods such as personalized weight aggregation assign higher weights to similar clients during aggregation. We pose a question: can a client benefit from other clients with dissimilar data distributions, and if so, how? This question is particularly relevant in scenarios with a high degree of non-IID, where clients have widely different distributions, and learning from only similar clients will result in a loss of knowledge from many other clients. We note that when dealing with clients with similar distributions, current methods tend to enforce their models to be close in the parameter space. It is reasonable to conjecture that a client can benefit from dissimilar clients if we allow their models to depart from each other. Based on this idea, we propose DiversiFed, which allows each client to learn from clients with diversified distribution. DiversiFed pushes personalized models of clients with dissimilar distributions apart in the parameter space while pulling together those with similar distributions. In addition, to achieve the above effect without using prior knowledge of distribution, we design a loss function that leverages model similarity to determine the degree of attraction and repulsion between any two models. Experiments on benchmark and medical datasets show that DiversiFed can outperform the state-of-the-art (SOTA) methods by up to 3.19%.