Web: http://arxiv.org/abs/2209.10083

Sept. 22, 2022, 1:11 a.m. | Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang

cs.LG updates on arXiv.org arxiv.org

Federated Learning (FL) is a machine learning paradigm that allows
decentralized clients to learn collaboratively without sharing their private
data. However, excessive computation and communication demands pose challenges
to current FL frameworks, especially when training large-scale models. To
prevent these issues from hindering the deployment of FL systems, we propose a
lightweight framework where clients jointly learn to fuse the representations
generated by multiple fixed pre-trained models rather than training a
large-scale model from scratch. This leads us to a …

arxiv federated learning pre-trained models

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