Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning with Non-IID Data
Author:
Affiliation:
1. Zhejiang University,College of Information Science and Electronic Engineering,Hangzhou,China,310027
2. VTT Technical Research Centre of Finland,Finland
3. University of Oulu,Centre for Wireless Communication,Finland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09839164.pdf?arnumber=9839164
Reference13 articles.
1. HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
2. Communication-efficient learning of deep networks from decentralized data;mcmahan;Proc Int Conf Artificial Intelligence Statistics,2017
3. Energy Efficient Federated Learning Over Wireless Communication Networks
4. A Low Complexity Interior-Point Algorithm for Linear Programming
5. Multi-level local SGD: Distributed SGD for heterogeneous hierarchical networks;castiglia;Proc ICLR,2021
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1. A Multi-Hop-Aware User To Edge-Server Association Game;2023 IEEE 9th International Conference on Network Softwarization (NetSoft);2023-06-19
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