Summary of Momentum Approximation in Asynchronous Private Federated Learning, by Tao Yu et al.
Momentum Approximation in Asynchronous Private Federated Learning
by Tao Yu, Congzheng Song, Jianyu Wang, Mona Chitnis
First submitted to arxiv on: 14 Feb 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 Momentum-based methods have been shown to produce the best model quality in synchronous federated learning (FL), but applying them to asynchronous protocols can result in slower convergence and degraded performance. To address this issue, we propose a momentum approximation method that minimizes the implicit bias introduced by asynchrony. This approach is compatible with secure aggregation and differential privacy, and can be easily integrated into production FL systems with minimal additional communication and storage costs. Our empirical results show that momentum approximation can achieve 1.15-4 times speedup in convergence compared to naively combining asynchronous FL with momentum. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) is a way for many devices to work together to learn from data, without sharing the data itself. Asynchronous protocols make it possible for more devices to participate in this process. Momentum-based methods can help improve the quality of the models learned through FL. However, combining these two techniques doesn’t always produce the best results. To solve this problem, researchers propose a new method that minimizes the bias caused by asynchrony. This method is compatible with existing security features and can be used in real-world applications without significant changes. The results show that this approach can make FL faster and more efficient. |
Keywords
* Artificial intelligence * Federated learning
