Authors - Manoj N, M Thanmay Ram, Manikanta S, Tarun Pradeep, Ramandeep Kaur Abstract - As IoT devices multiply in smart cities, safeguarding healthcare data's confidentiality, security, and integrity from various sources is getting harder. In order to protect healthcare data and facilitate effective machine learning, this article suggests a secure structure that combines Blockchain technology with Federated Learning (FL). With its immutable ledger, blockchain guarantees data confidentiality and openness throughout the network, whereas FL lets data stay on local devices, protecting privacy while training models. The suggested framework is ideal for smart city applications since it places a strong emphasis on safe data sharing, privacy protection, and dependable model management. The design tackles important problems like data breaches, illegal access, and confidence in model updates by utilizing FL's decentralized training and Blockchain's tamper-proof data management. This combination promotes openness and confidence among stakeholders while strengthening the security of healthcare data. The suggested method, which is intended for smart cities, opens the door for creative and privacy-compliant approaches to healthcare data administration and analysis by facilitating efficient collaboration across healthcare organizations without compromising patient privacy.
Open Zoom
