| Title |
A Review of Secure Healthcare Data Analytics using Federated Machine Learning and Blockchain Technology |
| Authors |
(Nandini Manickam);(Vijayakumar Ponnusamy) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.3.254 |
| 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. |