👉 Federated Graph Computing (fg computing) is an advanced approach to data processing and analytics that extends the concept of federated learning, traditionally used in machine learning, to graph-structured data. In fg computing, multiple decentralized devices or nodes collaborate to perform computations on large, distributed graph datasets without sharing the raw data itself. This method ensures privacy and security by keeping sensitive information local, while still enabling the collective analysis of interconnected data points. By leveraging edge computing and secure communication protocols, fg computing allows for efficient, scalable, and privacy-preserving graph analytics across diverse environments, from mobile devices to cloud infrastructures.
