Healthcare industry is harnessing AI and Machine learning for services like patient monitoring and disease diagnosis to assist health care practitioners in delivering accurate and timely medical results. The results can be further precise when multiple hospitals contribute their health care data for training a common ML model. Traditional Machine learning for such use cases involves aggregating the data from various hospitals in a central server where a ML model is trained to get the results. Security and privacy of data is a concern for sharing data as multiple participants are involved. The proposed work FedBlockSiamese uses Federated Learning which can be used for training machine learning models collaboratively with data from multiple clients but without leaving the client device. Thus, the privacy of the data is preserved through local model training on the client. The proposed work also utilizes blockchain to enhance security and trust in Federated Learning environment. The communication between client and server is verified with the help of Blockchain to prevent malicious clients purposefully sharing incorrect data. A Siamese Network is implemented in each client to classify the chest X-Ray images based on similarity score. These Siamese Networks exchange and update their model weights through a blockchain, allowing the Federated model to adapt to evolving threat landscapes. Our approach improves model security, adversarial attack resistance, and malicious client identification without compromising data privacy. Our findings demonstrate that the integration of Siamese Networks with Blockchain technology significantly enhances the resilience of Federated Learning systems against malicious actors. This innovative solution has the potential to revolutionize Federated Learning applications across various domains where data privacy and security are paramount concerns.
Ramamurthy et al. (Sat,) studied this question.