FedBlocks: federated learning and blockchainbased privacy-preserved pioneering framework for IoT healthcare using IPFS in web 3.0 era

The continual evolution of the digital health domain has led to an increased dependence on intelligent devices, such as smart bands and smartwatches, for monitoring personal well-being. These devices produce significant amounts of sensitive and personal data daily, underscoring the critical importance of safeguarding the privacy and security of this information. In response to this pressing challenge. This paper introduces a comprehensive framework incorporating deep learning, federated learning, IPFS (a secure data storage system), and blockchain technology. Our approach begins by utilizing deep learning to anonymize sensitive healthcare data, ensuring the protection of individual identities. Subsequently, this anonymized data is securely stored on IPFS, A decentralized and tamperproof data storage platform. We employ federated learning to improve models without exposing raw data, enabling distributed dataset training. Blockchain plays a crucial role by establishing transparent and immutable data access records, thereby enhancing security and accountability, a particularly critical aspect in healthcare where data integrity is paramount. The primary objective is to balance data privacy and usability for research purposes. Testing our framework with a well-established dataset (CIFAR-10) resulted in a high accuracy rate of 84.59% while preserving data privacy. This framework serves as a robust solution for protecting IoT healthcare data, integrating advanced technologies to meet the specific demands of the field. The implications of this research extend beyond current circumstances, offering a potential shift in the healthcare data handling paradigm. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.