Authors: Mahavani Kb, Bavithra Rs
Abstract: Federated learning has also become a paradigm shift to making machine learning collaborative and not centralized around sensitive data. Federated learning solves the increasing privacy, regulatory compliance, and data sovereignty concerns by preventing the transfer of model training to centralized model training clients, like hospitals, financial institutions, and IoT devices. Cloud platforms are critical to the operationalization of this paradigm as it offers scalable orchestration, secure aggregation, and communication-efficient frameworks. The paper discusses how cloud native federated learning systems decrease the amount of communication, enhance the model convergence, and provide more robust privacy guarantees without violating regulation of systems like GDPR and HIPAA. By applying federated learning to the medical diagnostic and financial fraud detection domains, the study shows that federated learning can be successful in providing a high level of model accuracy and strong privacy protection. The results indicate the significance of supporting federated learning by cloud-native infrastructure that will allow implementing privacy-safe AI solutions that can be widely adopted in regulated industries. From a privacy and regulatory perspective, cloud-based federated learning systems provide strong guarantees that align with data protection regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). By eliminating the need for raw data transfer, federated learning inherently supports privacy-by design principles. When combined with advanced privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption, federated learning further strengthens its compliance with strict legal and ethical requirements. To demonstrate the effectiveness of cloud-native federated learning, this study applies the proposed framework to two critical application domains: medical diagnosis and financial fraud detection. Experimental results show that federated models achieve performance levels comparable to, and in some cases exceeding, those of traditional centralized models, while significantly enhancing data privacy and security. In medical diagnostics, federated learning enables collaborative training across multiple healthcare institutions without exposing sensitive patient records. Similarly, in financial fraud detection, federated learning facilitates cross institutional intelligence sharing without compromising proprietary or customer data.
DOI:
