Accelerating the Decentralized Federated Learning via Manipulating Edges
Author:
Affiliation:
1. Shenzhen University, Shenzhen, Guangdong, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645509
Reference54 articles.
1. Accelerating Decentralized Federated Learning in Heterogeneous Edge Computing
2. Liangqi Yuan, Lichao Sun, Philip S Yu, and Ziran Wang. Decentralized federated learning: A survey and perspective. arXiv preprint arXiv:2306.01603, 2023.
3. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273--1282. PMLR, 2017.
4. Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
5. Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization
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