Summary of Adaptive Federated Learning Over the Air, by Chenhao Wang et al.
Adaptive Federated Learning Over the Air
by Chenhao Wang, Zihan Chen, Nikolaos Pappas, Howard H. Yang, Tony Q. S. Quek, H. Vincent Poor
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 federated version of adaptive gradient methods, such as AdaGrad and Adam, for over-the-air model training. The approach leverages the superposition property of wireless channels to facilitate fast and scalable parameter aggregation while enhancing robustness by adjusting the stepsize based on global gradients. The authors derive the convergence rate of the algorithms, considering channel fading and interference, for various nonconvex loss functions. The results show that AdaGrad-based methods converge at a rate of O(ln(T)/T^(1-1/α)) for heavy-tailed electromagnetic interference distributions, while Adam-like algorithms converge at O(1/T). Experimental results confirm the theoretical findings and demonstrate the practical effectiveness of federated adaptive gradient methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train models using wireless networks. It’s like having multiple computers talk to each other to learn from data together. This approach is important because it makes training faster and more reliable, especially when there are lots of noisy signals in the environment. The authors also show that certain types of noise can slow down the learning process, so they developed a way to adapt to this noise. They tested their method and found that it worked well in different situations. |
