FED-3DA: A Dynamic and Personalized Federated Learning Framework

In federated learning, the non-IID data generated from heterogeneous clients may reduce the global model efficiency. Previous studies use personalization as a common approach to adapt the global model to these clients (called the local model). However, client's data distribution may change dynamically with its location or environment, which can degrade the performance of the local model, leading to a new Dynamic Personalized Federated Learning (DPFL) problem. This paper proposes a novel approach to reduce the impact of the dynamic distribution on the local model based on metalearning and distribution distance measurement named Fed-3DA. It calculates the distribution distance periodically to perceive the distribution change on the client and adjust the local model preferences from a global meta-model through the distribution representation. Our experiments on public datasets show that Fed-3DA can effectively reduce the performance fluctuation of the local model in DPFL scenarios.