InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages

Author:

Li Qiushi1,Zhu Wenwu1,Wu Chao1,Pan Xinglin2,Yang Fan1,Zhou Yuezhi1,Zhang Yaoxue1

Affiliation:

1. Tsinghua University, Beijing, China

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

Funder

Key-Area Research and Development Program of Guangdong Province

National Natural Science Foundation of China

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Confidential Federated Learning for Heterogeneous Platforms against Client-Side Privacy Leakages;ACM Turing Award Celebration Conference 2024;2024-07-05

2. Gradient Coreset for Federated Learning;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

4. Federated Learning with Label-Masking Distillation;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

5. Locally private estimation of conditional probability distribution for random forest in multimedia applications;Information Sciences;2023-09

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