Summary of Mixed-precision Federated Learning Via Multi-precision Over-the-air Aggregation, by Jinsheng Yuan et al.
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation
by Jinsheng Yuan, Zhuangkun Wei, Weisi Guo
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this research paper, the authors propose a novel Over-the-Air Federated Learning (OTA-FL) framework that accommodates clients with varying hardware resources and bit precisions, leveraging approximate computing (AxC) for energy efficiency. The proposed mixed-precision OTA-FL framework optimizes the trade-off between server and client computational capabilities, energy consumption, and learning accuracy requirements. A multi-precision gradient modulation scheme is introduced to ensure compatibility with OTA aggregation and eliminate precision conversion overheads. Experimental results demonstrate a significant performance boost (up to 10% in ultra-low precision) and substantial energy savings (over 65%) compared to homogeneous standard precision OTA-FL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are working on a way for devices to learn together without sharing their data. They’re trying to make it work better by letting different devices use different amounts of computer power. This makes it more efficient and saves energy. The researchers developed a new system that can handle these differences and still get good results. They tested it and found that it works much better than the old way, especially for devices with very limited power. |
Keywords
» Artificial intelligence » Federated learning » Precision
