Incentive and Dynamic Client Selection for Federated Unlearning-Reference-Cited by-同舟云学术

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Incentive and Dynamic Client Selection for Federated Unlearning

Published:2024-05-13 Issue: Volume:56 Page:2936-2944
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Lin Yijing¹^ORCID,Gao Zhipeng¹^ORCID,Du Hongyang²^ORCID,Niyato Dusit²^ORCID,Kang Jiawen³^ORCID,Liu Xiaoyuan⁴^ORCID

Affiliation:

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

2. Nanyang Technological University, Singapore, Singapore

3. Guangdong University of Technology, Guangzhou, China

4. University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645462

Reference26 articles.

1. Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education

2. A survey;Xu Heng;ACM Computing Surveys,2023

3. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273--1282. PMLR, 2017.

4. FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models

5. The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining

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