Federated Learning and Artificial Intelligence in E-Healthcare
Meena Gupta, Priya Sharma, Ruchika Kalra · Advances in healthcare information systems and administration book series · 2023
Federated Learning (FL), a novel distributed interactive AI paradigm, holds particular promise for smart healthcare since it enables many clients including hospitals to take part in AI training while ensuring data privacy. Each participant's data that is sent to the server is really a trained sub-model rather than original data. FL benefits from better privacy features and dispersed data processing. Analysis of very sensitive data has substantially improved because to the combination of Federated Learning with healthcare data informatics. By utilizing the advantages of FL, the clients' data is preserved safely with their own model, and data leakage is avoided to prevent any malicious data modification in the system. Horizontal FL takes data from all devices with a comparable trait space suggests that Clients A and B are using the same features. Vertical Federated Learning uses a number of datasets from various feature domains to train a global model. A successful FL implementation could thus hold a significant potential for enabling precision medicine on a large scale.