Summary of Scalable and Resource-efficient Second-order Federated Learning Via Over-the-air Aggregation, by Abdulmomen Ghalkha et al.
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
by Abdulmomen Ghalkha, Chaouki Ben Issaid, Mehdi Bennis
First submitted to arxiv on: 10 Oct 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 The proposed paper presents a scalable second-order federated learning (FL) algorithm that leverages curvature information to achieve faster convergence, while addressing the challenges posed by high computational and storage costs. The approach utilizes a sparse Hessian estimate and over-the-air aggregation, making it suitable for larger models. In contrast to first-order baselines, the proposed method achieves significant communication resource and energy savings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new second-order FL algorithm that offers faster convergence than its first-order counterparts. To overcome the challenges of large-scale models, the algorithm uses a sparse Hessian estimate and over-the-air aggregation. This approach results in notable savings in communication resources and energy, making it an efficient solution for larger models. |
Keywords
» Artificial intelligence » Federated learning
