From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks

Understand

Machine learning (ML) tasks are becoming ubiquitous in today's network applications.

• Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data.
• There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.
• To address this, we advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.