ActPerFL: Active Personalized Federated Learning

no code implementations • FL4NLP (ACL) 2022 • Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.

Personalized Federated Learning Uncertainty Quantification

Self-Aware Personalized Federated Learning

no code implementations • 17 Apr 2022 • Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang

In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.

Personalized Federated Learning Uncertainty Quantification

Federated Learning Challenges and Opportunities: An Outlook

no code implementations • 1 Feb 2022 • Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs.

Federated Learning