Semi-decentralized federated edge learning for fast convergence on non-IID data

Y Sun, J Shao, Y Mao, JH Wang… - 2022 IEEE Wireless …, 2022 - ieeexplore.ieee.org
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …
被引用次数：36 相关文章

Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y Sun, J Shao, Y Mao, JH Wang, J Zhang - arXiv preprint arXiv …, 2021 - arxiv.org
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y Sun, J Shao, Y Mao, JH Wang, J Zhang - 2022 IEEE Wireless …, 2022 - dl.acm.org
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …
[PDF] github.io

[PDF][PDF] Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y Sun - hiyuchang.github.io
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data Page 1
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data Authors …

Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y Sun, J Shao, Y Mao, JH Wang, J Zhang - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data

Y Sun, J Shao, Y Mao, JH Wang… - 2022 IEEE Wireless …, 2022 - research.polyu.edu.hk
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …