Federated learning in cloud-edge-fog architectures: Enhancing privacy, efficiency, and scalability

Key Points

• Federated learning enhances privacy while enabling model training across decentralized systems, improving overall data security.
• Efficiency metrics indicate reduced latency by 30% using federated learning compared to traditional centralized models.
• Analysis of cloud-edge-fog architecture shows significant advancements in scalability for machine learning applications.
• Privacy concerns are increasingly addressed, highlighting the need for robust data security measures in networked environments.