Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification
arxiv(2024)
摘要
The application of deep learning techniques to medical problems has garnered
widespread research interest in recent years, such as applying convolutional
neural networks to medical image classification tasks. However, data in the
medical field is often highly private, preventing different hospitals from
sharing data to train an accurate model. Federated learning, as a
privacy-preserving machine learning architecture, has shown promising
performance in balancing data privacy and model utility by keeping private data
on the client's side and using a central server to coordinate a set of clients
for model training through aggregating their uploaded model parameters. Yet,
this architecture heavily relies on a trusted third-party server, which is
challenging to achieve in real life. Swarm learning, as a specialized
decentralized federated learning architecture that does not require a central
server, utilizes blockchain technology to enable direct parameter exchanges
between clients. However, the mining of blocks requires significant
computational resources, limiting its scalability. To address this issue, this
paper integrates the brain storm optimization algorithm into the swarm learning
framework, named BSO-SL. This approach clusters similar clients into different
groups based on their model distributions. Additionally, leveraging the
architecture of BSO, clients are given the probability to engage in
collaborative learning both within their cluster and with clients outside their
cluster, preventing the model from converging to local optima. The proposed
method has been validated on a real-world diabetic retinopathy image
classification dataset, and the experimental results demonstrate the
effectiveness of the proposed approach.
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