Federated Learning in Cloud Computing: A Framework for Privacy-Preserving AI Across Distributed Networks
Keywords:
Federated Learning, Cloud Computing, Privacy-Preserving AI, Distributed Networks, GDPR Compliance, Data PrivacyAbstract
In recent years, federated learning (FL) has emerged as a transformative approach within cloud computing to address privacy and security challenges associated with distributed artificial intelligence (AI). Unlike traditional centralized AI models, federated learning allows decentralized devices to collaboratively train models without sharing raw data, thus providing a privacy-preserving solution for various sectors such as healthcare, finance, and telecommunications. This paper explores the framework of federated learning within cloud computing, emphasizing its architecture, privacy mechanisms, and implementation challenges. By reviewing recent literature, this study provides a comprehensive understanding of federated learning’s potential to facilitate secure, efficient, and scalable AI in a distributed environment. Experimental results indicate that federated learning maintains comparable performance with centralized models while adhering to privacy regulations like GDPR, offering a viable alternative to centralized training paradigms.
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