Personalized Federated Learning

Federated learning faces challenges such as data heterogeneity, device heterogeneity, and communication efficiency, especially where data heterogeneity makes it difficult to train a single global model that is applicable to all clients. Personalized Federated Learning (PFL) aims to enhance the performance and practicality of models in heterogeneous environments by customizing methods to adapt the global model to the unique needs of each client.