Summary of Accelerating Federated Learning by Selecting Beneficial Herd Of Local Gradients, By Ping Luo et al.
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients
by Ping Luo, Xiaoge Deng, Ziqing Wen, Tao Sun, Dongsheng Li
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 the BHerd strategy, a novel approach for Federated Learning (FL) frameworks. FL is a distributed machine learning framework used in communication network systems, but its performance is negatively impacted by non-independent and identically distributed (Non-IID) data. The authors aim to select beneficial local gradients to accelerate model convergence. They map the distribution of local datasets to local gradients, use the Herding strategy to obtain a permutation, and then select the top-performing gradients for global aggregation. Experimental results demonstrate that BHerd effectively selects beneficial local gradients, mitigating Non-IID effects and accelerating model convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to make machine learning work better on different devices. They use something called Federated Learning (FL) which lets devices learn together without sharing their data. But when the data isn’t the same from device to device, it gets harder for machines to learn. The researchers want to fix this problem by picking the best information from each device and using it to help the machine learning model work better. They came up with a new way called BHerd that does this by looking at how similar the information is on each device. It works pretty well, and they showed that it can make machines learn faster! |
Keywords
* Artificial intelligence * Federated learning * Machine learning
