Federated Hyperparameter

Federated hyperparameter optimization (FedHPO) addresses the challenge of efficiently tuning hyperparameters in federated learning (FL) settings, where data resides on decentralized devices. Current research focuses on mitigating the noise introduced by data heterogeneity and communication limitations, exploring techniques like personalized hyperparameter networks and reinforcement learning-based approaches to optimize hyperparameters for individual clients or adaptively during training. Effective FedHPO is crucial for improving the performance and robustness of FL models across diverse applications, particularly in sensitive domains like healthcare where data privacy is paramount.