Summary of Non-convex Optimization in Federated Learning Via Variance Reduction and Adaptive Learning, by Dipanwita Thakur et al.
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning
by Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das
First submitted to arxiv on: 16 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 A novel federated learning algorithm is proposed to address non-convex problems across heterogeneous datasets. This approach combines momentum-based variance reduction with adaptive learning to minimize communication and computation overheads. The algorithm aims to overcome challenges related to gradient variance, which hinders model efficiency, and slow convergence resulting from learning rate adjustments with heterogeneous data. Experimental results on image classification tasks (MNIST, CIFAR-10) with heterogeneous datasets demonstrate the effectiveness of this approach in non-convex settings, achieving an improved communication complexity of O(ε^(-1)) compared to existing methods with a communication complexity of O(ε^(-2)). The proposed federated version balances convergence rate, number of communication rounds, and test accuracy while mitigating client drift in heterogeneous settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper suggests a new way for computers to work together (federated learning) when they have different types of data. It’s like trying to solve a puzzle with pieces from different boxes. The new method makes sure that the computers don’t waste time or resources by sharing information efficiently. It also helps the computers learn better and make fewer mistakes. The researchers tested this approach on image classification tasks and found it was effective, especially when dealing with different types of data. This could be useful for applications like self-driving cars or medical diagnosis. |
Keywords
» Artificial intelligence » Federated learning » Image classification
