ChronusFed: Reinforcement-Based Adaptive Partial Training for Heterogeneous Federated Learning

Author:

Xia Fuyuan1ORCID,Ying Chenhao1ORCID,Wei David S.L.2ORCID,Chen Wei1ORCID,Zhang Weiting3ORCID,Jin Haiming1ORCID,Luo Yuan1ORCID

Affiliation:

1. Shanghai Jiao Tong University, China

2. Fordham University, United States of America

3. Beijing Jiaotong University, China

Publisher

ACM

Reference31 articles.

1. Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang. 2022. Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction. Advances in neural information processing systems 35 (2022), 29677–29690.

2. FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning

3. A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee

4. Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, and Dimitrios Dimitriadis. 2022. Heterogeneous ensemble knowledge transfer for training large models in federated learning. arXiv preprint arXiv:2204.12703 (2022).

5. Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter. 2017. A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017).

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