Summary of Brain Storm Optimization Based Swarm Learning For Diabetic Retinopathy Image Classification, by Liang Qu et al.
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification
by Liang Qu, Cunze Wang, Yuhui Shi
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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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 paper explores the application of deep learning techniques in medical problems, particularly in medical image classification tasks. However, due to data privacy concerns, hospitals are hesitant to share their private data to train an accurate model. Federated learning, a promising solution, balances data privacy and model utility by training models on client devices while aggregating uploaded parameters. Despite its potential, federated learning relies on a trusted central server, which is challenging in real-life scenarios. Swarm learning, a decentralized approach, uses blockchain technology for direct parameter exchanges between clients but requires significant computational resources, limiting scalability. To address this limitation, the paper proposes BSO-SL (Brain Storm Optimization- Swarming Learning), integrating the brain storm optimization algorithm into the swarm learning framework. This approach clusters similar clients and enables collaborative learning within and outside their cluster to prevent local optima convergence. The proposed method is validated on a diabetic retinopathy image classification dataset, demonstrating its effectiveness in balancing data privacy and model utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use artificial intelligence (AI) to help with medical problems, like classifying images of eyes. A big challenge is that hospitals don’t want to share their private information to make this work. The authors explore two solutions called federated learning and swarm learning. Federated learning is when many computers train a single model together while keeping their own data private. However, this relies on having a trustworthy central hub. Swarm learning uses blockchain technology to let computers exchange information directly without needing a central hub. But, this method requires a lot of computing power and isn’t very efficient. To solve this problem, the authors propose a new way called BSO-SL that combines the two methods. This allows similar computers to work together and share their knowledge with other computers to create more accurate models. The paper tests this new approach on real-world data and shows it’s effective in balancing privacy and accuracy. |
Keywords
» Artificial intelligence » Deep learning » Federated learning » Image classification » Optimization
