Federated Learning on Cloud Platforms: Privacy-Preserving AI for Distributed Data
DOI:
https://doi.org/10.21590/ijtmh.7.03.06Keywords:
Federated Learning, Cloud Platforms, Privacy-Preserving AI, Distributed Data, Secure Aggregation, GDPR, HIPAA, Healthcare AI, Financial Fraud Detection, Cloud-Native ArchitecturesAbstract
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.
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