Advances and Open Challenges in Federated Learning with Foundation Models

no code implementations • 23 Apr 2024 • Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, Qiang Yang

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.

Computational Efficiency Federated Learning +1

Towards Personalized Federated Learning

no code implementations • 1 Mar 2021 • Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang

In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy.

Benchmarking Personalized Federated Learning +1