Summary of Whale-fl: Wireless and Heterogeneity Aware Latency Efficient Federated Learning Over Mobile Devices Via Adaptive Subnetwork Scheduling, by Huai-an Su et al.
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling
by Huai-an Su, Jiaxiang Geng, Liang Li, Xiaoqi Qin, Yanzhao Hou, Hao Wang, Xin Fu, Miao Pan
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Image and Video Processing (eess.IV)
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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 The proposed federated learning approach, called Wireless and Heterogeneity Aware Latency Efficient FL (WHALE-FL), aims to accelerate the training process by adapting subnetwork size assignments for mobile devices based on their dynamic computing and communication conditions. This is in contrast to traditional fixed-size subnetwork assignment methods that ignore these changes. The authors develop a novel utility function that captures device and federated learning dynamics, guiding mobile devices to select the optimal subnetwork size for local training. This approach enables faster and more accurate federated learning without sacrificing learning accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning over mobile devices is an exciting area of research! Currently, it’s challenging to deploy this technology because different devices have varying computing and communication abilities. Some researchers suggested using smaller parts of the global model for each device to train locally, but this approach doesn’t consider how devices change or the training process itself. To solve these issues, a new method called WHALE-FL was created. It uses a special formula to help devices choose the right size of their local training pieces based on their abilities and the learning process. This makes federated learning faster and more accurate. |
Keywords
» Artificial intelligence » Federated learning
