Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression-Reference-Cited by-同舟云学术

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Published:2024-05-20 Issue: Volume:34 Page:1271-1280
ISSN:
Container-title:IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
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Author:

Su Xiaoxin¹,Zhou Yipeng²,Cui Laizhong¹,Guo Song³

Affiliation:

1. Shenzhen University,College of Computer Science and Software Engineering,Shenzhen,China

2. Macquarie University,School of Computing, Faculty of Science and Engineering,Sydney,Australia

3. The Hong Kong University of Science and Technology,Department of Computer Science and Engineering,HKSAR

Funder

Research and Development

National Natural Science Foundation of China

Impact Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10621050/10621073/10621376.pdf?arnumber=10621376

Reference38 articles.

1. Transformer in Transformer;Han;Advances in Neural Information Processing Systems (NeurIPS),2021

2. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

3. An In-Network Parameter Aggregation using DPDK for Multi-GPU Deep Learning

4. A2TP: Aggregator-aware In-network Aggregation for Multi-tenant Learning

5. Scaling Distributed Machine Learning with In-Network Aggregation;Sapio

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