Billion-scale federated learning on mobile clients-Reference-Cited by-同舟云学术

Billion-scale federated learning on mobile clients

Published:2020-09-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th Annual International Conference on Mobile Computing and Networking
language:
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Author:

Niu Chaoyue¹,Wu Fan¹,Tang Shaojie²,Hua Lifeng³,Jia Rongfei³,Lv Chengfei³,Wu Zhihua³,Chen Guihai¹

Affiliation:

1. Shanghai Jiao Tong University, China

2. University of Texas at Dallas

3. Alibaba Group, China

Funder

National Key R&D Program of China

Alibaba Group

National Natural Science Foundation of China

Joint Scientific Research Foundation of the State Education Ministry

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3372224.3419188

Reference57 articles.

1. Deep Learning with Differential Privacy

2. Alimama. 2017. Ad Display/Click Data on Taobao.com. https://tianchi.aliyun.com/dataset/dataDetail?dataId=56. Alimama. 2017. Ad Display/Click Data on Taobao.com. https://tianchi.aliyun.com/dataset/dataDetail?dataId=56.

3. PIR with Compressed Queries and Amortized Query Processing

4. Learning with Privacy at Scale;Privacy Team Apple's Differential;Apple Machine Learning Journal,2017

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