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Title A Review of Secure Healthcare Data Analytics using Federated Machine Learning and Blockchain Technology
Authors (Nandini Manickam);(Vijayakumar Ponnusamy)
Page pp.254-262
ISSN 2287-5255
Keywords Federated learning (FL); Blockchain; Data privacy; Accuracy; Computational speed
Abstract In recent trends of growth in technologies, data management, maintenance of medical records, sharing of data, diagnosis of disease, and medication are the key areas where digital healthcare plays a vital role. Despite enormous improvement, handling huge amounts of data, privacy, secure sharing, accuracy, and computational speed remains challenging. Federated learning is a machine learning technology that allows distributed model training using users’ own data to train a model. The model update is done through a central server that aggregates individual users and sends a global model. This ensures privacy protection and is suitable for handling large data. Blockchain technology is a publicly distributed ledger that collects the information of nodes as blocks and sends a copy to all nodes in the network so that data transparency is maintained and secure. However, blockchain has a limitation in handling large volumes of data. In such cases, federated learning can be used with a blockchain for better performance. By integrating federated learning with blockchain, accurate prediction, computational speed, data security, privacy, and accuracy can be achieved. A comprehensive review of how various federated learning technologies can integrate with blockchain networks to achieve accuracy and efficiency is presented.