SK-Gradient: Efficient Communication for Distributed Machine Learning with Data Sketch-Reference-Cited by-同舟云学术

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SK-Gradient: Efficient Communication for Distributed Machine Learning with Data Sketch

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 39th International Conference on Data Engineering (ICDE)
language:
Short-container-title:

Author:

Gui Jie¹,Song Yuchen¹,Wang Zezhou¹,He Chenhong¹,Huang Qun¹

Affiliation:

1. Peking University

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10184508/10184509/10184851.pdf?arnumber=10184851

Reference82 articles.

1. SketchVisor

2. Space-efficient online computation of quantile summaries

3. SketchML

4. Fedsketch: Communication-efficient and private federated learning via sketching;haddadpour,2020

5. Deep Residual Learning for Image Recognition

Cited by 3 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. MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. HistSketch: A Compact Data Structure for Accurate Per-Key Distribution Monitoring;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

3. KVSAgg: Secure Aggregation of Distributed Key-Value Sets;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

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