Enhancing Artificial Intelligence in Healthcare, Finance and Smart City Infrastructure: Development with Privacy Preservation using Blockchain-Enabled Federated Learning Frameworks

As Artificial Intelligence (AI) becomes a more pervasive presence in high-value domains like healthcare, finance, and smart city infrastructure, it is fundamentally altering decision-making and service provision across society. But increased dependence on giant pools of sensitive data raises major issues about data privacy, data security, and regulatory compliance. We explore the integration of Federated Learning (FL) and Blockchain Technology as a strong framework to balance AI advancement and data privacy in distributed systems. In BEFL, each participant trains the model on their local data to avoid sending raw data as well as centralization and uses the blockchain mechanism to ensure the necessary transparency, traceability and trust of the other participants involved. For instance, in healthcare, where patient data confidentiality is of utmost importance, BEFL frameworks support institutions in joint model training for disease Diag, medical imaging, and treatment recommendation, etc., without compromising patient privacy, or breaking data sharing laws such as HIPAA or GDPR. Likewise, in the banking and finance industry, where data integrity and auditability are paramount, the combination of blockchain and federated learning can lead to creating fraud detection systems, risk assessment tools, and predictive analytics models, which can learn from a wide variety of datasets without revealing sensitive or proprietary financial information. This secure and distributed approach to AI development can be applied across smart city ecosystems, where a myriad of connected devices, sensors, and services— enabling intelligent and secure traffic optimization, energy management, and emergency response systems—can be created. In this paper, we propose an architectural design of the blockchain-based federated learning framework, introduce the main technical challenges, and present the corresponding mitigation strategies, including secure aggregation, differential privacy, and smart contract-based governance, etc.