Summary of Client Contribution Normalization For Enhanced Federated Learning, by Mayank Kumar Kundalwal et al.
Client Contribution Normalization for Enhanced Federated Learning
by Mayank Kumar Kundalwal, Anurag Saraswat, Ishan Mishra, Deepak Mishra
First submitted to arxiv on: 10 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 The proposed paper addresses the challenges of decentralized data generated from mobile devices, which can impede traditional centralized machine learning models due to communication costs and privacy risks. Federated Learning (FL) offers a promising alternative by enabling collaborative training without sharing data. However, FL faces challenges due to statistical heterogeneity among clients, where non-IID data impedes model convergence and performance. The paper proposes a novel approach leveraging mean latent representations extracted from locally trained models to normalize client contributions and mitigate limitations of conventional federated averaging methods. The proposed method introduces a normalization scheme using mean latent representations to handle statistical heterogeneity in FL, demonstrating seamless integration with existing FL algorithms to improve performance in non-IID settings. The approach is validated through extensive experiments on diverse datasets, showing significant improvements in model accuracy and consistency across skewed distributions. The results also highlight the robustness of the proposed approach with six FL schemes: FedAvg, FedProx, FedBABU, FedNova, SCAFFOLD, and SGDM. The paper contributes to the development of more reliable and generalized machine learning models by providing a practical and computationally efficient solution for statistical heterogeneity in FL. By leveraging mean latent representations, the proposed approach can be used to improve performance in non-IID settings, making it a valuable addition to the FL literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make artificial intelligence work better on phones and other devices that are connected to the internet. Right now, these devices generate lots of data that is hard to use because it’s all over the place and not organized in one place. Federated Learning (FL) is a way to solve this problem by letting devices work together without sharing their data. But FL has its own challenges, like when the devices don’t have the same kind of data. The researchers propose a new method that uses special representations to make sure all the devices are working together in harmony. They tested it with six different ways of doing FL and showed that it works really well. This is important because it can help create better artificial intelligence models that can be used on phones and other devices. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
