Summary of Esfl: Efficient Split Federated Learning Over Resource-constrained Heterogeneous Wireless Devices, by Guangyu Zhu et al.
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
by Guangyu Zhu, Yiqin Deng, Xianhao Chen, Haixia Zhang, Yuguang Fang, Tan F. Wong
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel approach to efficient Federated Learning (FL) by introducing an Efficient Split Federated Learning algorithm (ESFL). The ESFL splits the machine learning model between devices and a central server, enabling joint optimization of user-side workload and server-side computing resource allocation. This is achieved through a mixed-integer non-linear program formulation, which is NP-hard, but the authors develop an iterative approach to obtain an efficient approximate solution. Experimental results demonstrate that the ESFL significantly outperforms standard FL, split learning, and splitfed learning in terms of efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) helps devices work together without sharing their personal data. Imagine if your phone or smartwatch could help create a new AI model just by processing some information locally. But, it’s hard to make this efficient and fair for all devices involved. This paper creates a new way called ESFL (Efficient Split Federated Learning) that makes the most of both device power and central server computing. By splitting the AI model into smaller parts, ESFL can balance what each device does with how much work is done on the server. The results show that this approach is much faster than other methods. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Optimization
