FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy-Reference-Cited by-同舟云学术

FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy

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

Chen Dong¹^ORCID,Tang Siliang¹^ORCID,Shen Zijin¹^ORCID,Wang Guoming¹^ORCID,Xiao Jun¹^ORCID,Zhuang Yueting¹^ORCID,Yang Carl²^ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Emory University, Atlanta, GA, USA

Funder

Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Ant Group

the NSFC

the Zhejiang NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611953

Reference45 articles.

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3. DEFEATnet—A Deep Conventional Image Representation for Image Classification

4. Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 ( 2014 ). Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

5. Topology-aware Differential Privacy for Decentralized Image Classification