#Personalized Federated Learning

This project provides a robust framework for assessing advanced Federated Learning methods focused on user data privacy. It covers a range of Federated Learning approaches, including traditional, personalized, and domain generalization techniques like FedAvg, FedProx, pFedSim, and FedSR. With detailed environment setup instructions, including PyPI and Docker, and a step-by-step guide for experiments, the benchmark supports thorough exploration and application of FL strategies. It also offers parallel training through Ray and visualization capabilities with Visdom and Tensorboard, aiding precise optimization of FL models.