Summary of Heterogeneity-aware Resource Allocation and Topology Design For Hierarchical Federated Edge Learning, by Zhidong Gao et al.
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
by Zhidong Gao, Yu Zhang, Yanmin Gong, Yuanxiong Guo
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 A Federated Learning (FL) framework for mobile edge devices is presented in this paper. Traditional FL algorithms like FedAvg require a high communication workload on these devices. To address this issue, Hierarchical Federated Edge Learning (HFEL) leverages edge servers as intermediaries for model aggregation. However, HFEL faces challenges such as slow convergence rate and high resource consumption, especially with system and data heterogeneity. The focus is shifted to improving training efficiency in traditional FL, leaving the efficiency of HFEL largely unexplored. This paper proposes a two-tier HFEL system where edge devices are connected to edge servers, and edge servers interact through peer-to-peer (P2P) edge backhauls. The goal is to enhance training efficiency through strategic resource allocation and topology design. An optimization problem is formulated to minimize total training latency by allocating computation and communication resources and adjusting P2P connections. To ensure convergence under dynamic topologies, the paper analyzes the convergence error bound and introduces a model consensus constraint into the optimization problem. The proposed problem is decomposed into subproblems, allowing for online solution. This method facilitates efficient large-scale FL at edge networks with data and system heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make training faster on mobile devices using Federated Learning (FL). Current methods like FedAvg take too much communication power from these devices. The authors propose a new way called Hierarchical Federated Edge Learning (HFEL) that uses edge servers as helpers for model mixing. But HFEL also has its own problems, such as taking too long to finish and using up too many resources, especially when there’s data or system differences. The paper focuses on making FL faster in general, but doesn’t explore how to make HFEL better. Instead, it suggests a new way to organize the devices and servers that helps with speed and efficiency. |
Keywords
» Artificial intelligence » Federated learning » Optimization
