This review examines federated learning and its ability to secure and scale data intelligence in hybrid cloud systems, implying critical advancements in data privacy.
Key Points
The review aims to analyze federated learning implementations within hybrid cloud environments, focusing on security, privacy, and scalability.
Literature analysis of publications between 2016 and 2024
Examination of architectural frameworks and deployment strategies
Analysis of security and privacy challenges from various perspectives including technical and regulatory
Federated learning in hybrid clouds presents significant privacy-preserving analytics opportunities
Unique challenges in communication efficiency and orchestration emerge
Layered security architectures are essential for effective implementation