Summary of Fedqp: Towards Accurate Federated Learning Using Quadratic Programming Guided Mutation, by Jiawen Weng et al.
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation
by Jiawen Weng, Zeke Xia, Ran Li, Ming Hu, Mingsong Chen
First submitted to arxiv on: 24 Nov 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 This paper addresses the challenge of achieving high-inference performance in Federated Learning (FL) systems, which are widely used due to their privacy-preserving nature. The existing FL methods suffer from low-performance issues caused by data heterogeneity, where local models have different optimization directions due to varying data distributions. To tackle this problem, the paper proposes a novel mutation-based FL approach called FedQP, which utilizes quadratic programming to regulate the mutation directions wisely. By biasing model mutation towards gradient update direction rather than traditional random mutation, FedQP guides the model to optimize towards a well-generalized area (i.e., flat area). Experimental results on multiple datasets demonstrate that the proposed strategy effectively improves inference accuracy in heterogeneous data scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine learning more private and efficient. Right now, when many devices learn together, they don’t work very well because their data is different. The authors of this paper came up with a new way to help these devices learn from each other better. They use something called “mutation” to move the model around in a special way that helps it get better at making predictions. This works especially well when the data is very different between devices. The authors tested their method on some real datasets and found that it makes the model more accurate. |
Keywords
* Artificial intelligence * Federated learning * Inference * Machine learning * Optimization
