Sparse Communication for Federated Learning-Reference-Cited by-同舟云学术

Sparse Communication for Federated Learning

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)
language:
Short-container-title:

Author:

Thonglek Kundjanasith¹,Takahashi Keichi²,Ichikawa Kohei¹,Nakasan Chawanat³,Leelaprute Pattara⁴,Iida Hajimu¹

Affiliation:

1. Nara Institute of Science and Technology,Nara,Japan

2. Tohoku University,Sendai,Japan

3. Kanazawa University,Ishikawa,Japan

4. Kasetsart University,Bangkok,Thailand

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9798892/9798896/09799177.pdf?arnumber=9799177

Reference25 articles.

1. FedBoost: A communication-efficient algorithm for federated learning;hamer;Proceedings of the International Conference on Machine Learning (ICML) H D III and A Singh Eds,2020

2. Federated learning with additional mechanisms on clients to reduce communication costs;yao;CoRR,2019

3. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

4. Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

5. Federated learning: Strategies for improving communication efficiency;kone?ny;Proceedings of the NIPS Private Multi-Party Machine Learning Workshop,2016

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. Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions;Future Internet;2023-12-14

2. Privacy-Preserving Machine Learning for Snoring Detection;2023 11th International Conference on Information and Education Technology (ICIET);2023-03-18

3. Random Orthogonalization for Federated Learning in Massive MIMO Systems;IEEE Transactions on Wireless Communications;2023