Summary of Pre-training and Personalized Fine-tuning Via Over-the-air Federated Meta-learning: Convergence-generalization Trade-offs, by Haifeng Wen et al.
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
by Haifeng Wen, Hong Xing, Osvaldo Simeone
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates a novel approach to personalized federated learning (FL) for wireless settings, where agents participate in pre-training and fine-tuning processes. The proposed meta-learning-based personalized FL (meta-pFL) aims to generalize well to new agents and tasks while balancing convergence with channel impairments. To achieve this, the authors adopt over-the-air computing and study the trade-off between generalization and convergence. Experimental results validate the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to make artificial intelligence models work better together in wireless networks. The idea is called personalized federated learning (FL), which lets different devices learn from each other’s experiences. But how does it work? The authors use something called meta-learning, which allows devices to adapt to new situations and tasks. They study how this works in a wireless setting where devices are connected through the airwaves. They find that making small changes can help models generalize better to new situations while still learning quickly. This is important for many applications, like language translation or autonomous vehicles. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Generalization » Meta learning » Translation
