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This paper explores the integration of federated learning within cloud computing to enhance data privacy in AI applications. It addresses the challenges posed by centralized data storage, including data breaches and unauthorized access. By utilizing federated learning, organizations can train AI models collaboratively without compromising sensitive information, thus fostering innovation while ensuring compliance with privacy regulations. The framework presented outlines the technical, ethical, and regulatory aspects of this approach, highlighting its potential to revolutionize data-driven AI applications while maintaining user trust and data security.
Click here: Cloud-based AI systems and proposes
Josh Mckenna Thomas, a doctoral candidate at Atlantic International University, presents a comprehensive analysis of privacy concerns in cloud-based AI systems. His research emphasizes the vulnerabilities associated with centralized data storage and the pressing need for innovative solutions. By proposing federated learning as a viable alternative, Thomas illustrates how organizations can leverage this technology to enhance data privacy while still harnessing the power of AI. The study delves into the technical, ethical, and regulatory implications of federated learning, providing a structured framework for understanding its benefits and challenges. This work contributes significantly to the discourse on data privacy in AI, offering insights that are crucial for organizations navigating the complexities of cloud computing.
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