FedGH: Heterogeneous Federated Learning with Generalized Global Header-Reference-Cited by-同舟云学术

FedGH: Heterogeneous Federated Learning with Generalized Global Header

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Yi Liping¹^ORCID,Wang Gang¹^ORCID,Liu Xiaoguang¹^ORCID,Shi Zhuan²^ORCID,Yu Han³^ORCID

Affiliation:

1. College of C.S., DISSec, GTIISC, Nankai University, Tianjin, China

2. School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, China

3. School of Computer Science and Technology, University of Science and Technology of China (USTC), Singapore, Singapore

Funder

Joint NTU-WeBank Research Centre on Fintech

Nanyang Assistant Professorship (NAP)

National Science Foun- dation of China

National Research Foundation Singapore and DSO National Lab- oratories under the AI Singapore Programme

Fundamental Research Funds for the Central Universities

RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611781

Reference49 articles.

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2. Jin-Hyun Ahn et al. 2020. Cooperative Learning VIA Federated Distillation OVER Fading Channels . In Proc. ICASSP. IEEE , Barcelona, Spain, 8856--8860. Jin-Hyun Ahn et al. 2020. Cooperative Learning VIA Federated Distillation OVER Fading Channels. In Proc. ICASSP. IEEE, Barcelona, Spain, 8856--8860.

3. Samiul Alam et al. 2022. FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction . In Proc. NeurIPS., virtual. Samiul Alam et al. 2022. FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction. In Proc. NeurIPS., virtual.

4. Representation Learning: A Review and New Perspectives

5. Hongyan Chang et al. 2021. Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer . In Proc. NeurIPS Workshop., virtual. Hongyan Chang et al. 2021. Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. In Proc. NeurIPS Workshop., virtual.

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