Summary of Over-the-air Federated Learning in Cell-free Mimo with Long-term Power Constraint, by Yifan Wang et al.
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
by Yifan Wang, Cheng Zhang, Yuanndon Zhuang, Mingzeng Dai, Haiming Wang, Yongming Huang
First submitted to arxiv on: 7 Oct 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 The paper proposes an optimization algorithm, MOP-LOFPC, for Over-the-Air Federated Learning in Cell-free MIMO systems. It derives error bounds for this application and formulates a problem to minimize the optimality gap through joint power control and beamforming optimization. The algorithm uses Lyapunov optimization to decouple long-term constraints across rounds while only requiring causal channel state information. Experimental results show that MOP-LOFPC achieves a better trade-off between training loss and power constraint adherence compared to existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make wireless networks more intelligent by improving how they learn from artificial intelligence. It focuses on a special way of sharing learning between devices called Over-the-Air Federated Learning. The researchers developed a new algorithm, MOP-LOFPC, that can help this process work better and use less power. They tested their algorithm and showed it works better than other methods. |
Keywords
» Artificial intelligence » Federated learning » Optimization
