Summary of Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation For Hierarchical Federated Learning, by Rung-hung Gau et al.
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning
by Rung-Hung Gau, Ting-Yu Wang, Chun-Hung Liu
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles hierarchical federated learning with mobile users, edge servers, and a cloud server. The authors aim to minimize global rounds by solving a combinatorial optimization problem. They propose the twin sorting dynamic programming (TSDP) algorithm for two edge servers, which finds an optimal solution in polynomial time. For three or more edge servers, they introduce the TSDP-assisted algorithm for user association. Additionally, they formulate and solve a convex optimization problem for wireless bandwidth allocation given a user association matrix. The results show that their approach outperforms other schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers work on making a type of machine learning system more efficient. They want to reduce the time it takes for all devices involved in the system to share information with each other. To do this, they create new algorithms and solve special math problems. These solutions help decide which devices should be connected to each other and how much data they can send at a time. The results show that their methods are better than others. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Optimization
