Summary of Delayed Random Partial Gradient Averaging For Federated Learning, by Xinyi Hu
Delayed Random Partial Gradient Averaging for Federated Learning
by Xinyi Hu
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper proposes a novel approach to enhance federated learning (FL), a distributed machine learning paradigm that enables collaborative model training while preserving privacy. The scaling of real-world FL systems is limited by communication bottlenecks, particularly high latency costs and large Deep Neural Networks (DNNs). To address these issues, the authors introduce Delayed Random Partial Gradient Averaging (DPGA), which reduces system run time by enabling parallel computation and communication. DPGA works by having clients share partial local model gradients with the server, where the size of the shared part is determined by an update rate that is refined over time. Experimental results on non-IID CIFAR-10/100 demonstrate the efficacy of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning called federated learning. Federated learning lets many devices learn together without sharing their private information. But, right now, making it work is hard because it takes too long and uses too much data. The authors came up with a new way to make it faster and better. They call it Delayed Random Partial Gradient Averaging, or DPGA for short. It lets devices share only what they need to share, which makes everything go smoother and faster. The authors tested this method on some real-world problems and showed that it works really well. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
