Summary of Energy-efficient Federated Edge Learning with Streaming Data: a Lyapunov Optimization Approach, by Chung-hsuan Hu et al.
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
by Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT); Signal Processing (eess.SP)
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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 addresses the challenges in federated edge learning (FEEL) systems by developing a dynamic scheduling and resource allocation algorithm for efficient training of machine learning models. The proposed scheme considers the time-varying nature of wireless channels, random data arrivals, and energy constraints to optimize device scheduling, computational capacity adjustment, and bandwidth and transmit power allocation. The authors formulate a stochastic network optimization problem using the Lyapunov drift-plus-penalty framework and provide convergence analysis for heterogeneous data and time-varying objective functions. The results demonstrate improved learning performance and energy efficiency compared to baseline schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to train artificial intelligence models on many devices without sharing personal information. This paper tries to solve that problem by developing a new way to manage resources and decide when to train the models. It takes into account things like how data is arriving at each device, the quality of the connection between devices, and the energy used. The goal is to make sure the training happens efficiently and accurately while respecting people’s privacy. |
Keywords
» Artificial intelligence » Machine learning » Optimization
