Summary of Generative Ai-powered Plugin For Robust Federated Learning in Heterogeneous Iot Networks, by Youngjoon Lee et al.
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
by Youngjoon Lee, Jinu Gong, Joonhyuk Kang
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 federated learning (FL) that addresses the issue of non-independent and identically distributed (Non-IID) data distribution across devices. The authors develop a plugin for federated optimization techniques that uses generative AI-enhanced data augmentation and balanced sampling strategy to approximate Non-IID data distributions to IID. This is achieved by synthesizing additional data for underrepresented classes on each edge device, leveraging generative AI to create a more balanced dataset across the FL network. The authors also introduce a balanced sampling approach at the central server that selectively includes only the most IID-like devices, accelerating convergence while maximizing the global model’s performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are working on making it easier for different devices to work together and learn from each other without having to share their data. This is important because some devices might have very different types of data, which can make it hard for them to learn from each other. The authors came up with a new way to make sure that all the devices are working together smoothly by using artificial intelligence to create more balanced datasets and selecting only the most similar devices to include in the learning process. This approach helps the model converge faster and perform better, even when there is limited data available. |
Keywords
» Artificial intelligence » Data augmentation » Federated learning » Optimization
