FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients-Reference-Cited by-同舟云学术

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients

Published:2024-06-03 Issue: Volume:1 Page:398-411
ISSN:
Container-title:Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services
language:
Short-container-title:

Author:

Shen Leming¹^ORCID,Yang Qiang¹²^ORCID,Cui Kaiyan¹³^ORCID,Zheng Yuanqing¹^ORCID,Wei Xiao-Yong¹⁴^ORCID,Liu Jianwei⁵^ORCID,Han Jinsong⁶^ORCID

Affiliation:

1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

2. Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom

3. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

4. School of Computer Science, Sichuan University, Chengdu, China

5. College of Computer Science and Technology, School of Cyber Science and Technology, Zhejiang University, Hangzhou, China

6. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

Funder

Hong Kong GRF Grant

National Natural Science Foundation of China

Pioneer and Leading Goose R&D Program of Zhejiang

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643832.3661880

Reference88 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S Corrado Andy Davis Jeffrey Dean Matthieu Devin et al. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems.

2. Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization

3. Andrei Afonin and Sai Praneeth Karimireddy. 2021. Towards model agnostic federated learning using knowledge distillation. arXiv preprint arXiv:2110.15210 (2021).

4. 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.

5. Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane. 2020. Flower: A Friendly Federated Learning Research Framework. CoRR (2020).