Summary of Gp-fl: Model-based Hessian Estimation For Second-order Over-the-air Federated Learning, by Shayan Mohajer Hamidi et al.
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
by Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad, H. Vincent Poor
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a federated learning setting, second-order methods are crucial to improve the convergence rate of learning algorithms. However, these methods require clients to share their local Hessian matrices with the parameter server (PS), which incurs a significant communication cost. To address this issue, this paper introduces a novel second-order FL framework tailored for wireless channels. The key innovation lies in the PS’s ability to directly estimate the global Hessian matrix from received noisy local gradients using a non-parametric method. This method models the unknown Hessian matrix as a Gaussian process and leverages temporal relations between gradients and Hessian, along with the channel model, to obtain a stochastic estimator for the global Hessian matrix. Dubbed Gaussian process-based Hessian modeling for wireless FL (GP-FL), this approach exhibits a linear-quadratic convergence rate. Numerical experiments demonstrate that GP-FL outperforms classical baseline first and second-order FL approaches on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to improve how computers learn from many devices at the same time. This is important because it can help computers do things faster and more accurately. The problem is that these devices need to share information with each other, which takes up a lot of space on the internet. To fix this, the researchers created a new method that allows devices to work together without sharing as much information. They used a special kind of math called Gaussian processes to figure out how all the devices are connected and then used that to improve the way they learn from each other. |
Keywords
» Artificial intelligence » Federated learning
