Summary of Boosting Fairness and Robustness in Over-the-air Federated Learning, by Halil Yigit Oksuz et al.
Boosting Fairness and Robustness in Over-the-Air Federated Learning
by Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler, Joerg Raisch
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Over-the-Air federated learning algorithm for decentralized machine learning model training that prioritizes fairness and robustness through minmax optimization. By employing the epigraph form of the problem, the proposed approach converges to the optimal solution, outperforming state-of-the-art methods in terms of efficiency and privacy. The algorithm’s ability to bypass complex encoding-decoding schemes also enhances its scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way for devices to learn together over the airwaves without sharing their individual data. This approach is important because it helps ensure that each device gets an equal say in the learning process and keeps its information private. The method uses a special form of optimization called minmax, which is designed to balance fairness and robustness. The result is a more efficient and secure way for devices to collaborate and learn from each other. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Optimization
