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Advances and Open Challenges in Federated Learning with Foundation Models
April 25, 2024, 7:41 p.m. | Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Anran Li, Yulan Gao, Alysa Ziying Tan, Bo Zhao, Xiaoxiao Li, Zengxiang Li, Qiang Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: 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. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of …
abstract advances artificial artificial intelligence arxiv capabilities challenges computational concerns cs.ai cs.lg data decentralization efficiency federated learning foundation integration intelligence paper paradigm privacy survey type while
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