Scaling Federated Learning Systems for Privacy Preserving Intelligence in Distributed Cloud Environments
Introduction Federated Learning (FL) has emerged as a compelling paradigm for training machine learning models across a multitude of devices or silos without moving raw data. By keeping data locally and exchanging only model updates, FL addresses stringent privacy regulations, reduces bandwidth consumption, and enables collaborative intelligence across organizations that would otherwise be unwilling or unable to share proprietary datasets. However, moving from a research prototype to a production‑grade system that spans thousands to millions of edge devices, edge gateways, and cloud data centers introduces a new set of engineering challenges. Scaling FL in distributed cloud environments demands careful orchestration of communication, robust privacy‑preserving mechanisms, fault‑tolerant infrastructure, and efficient resource management. ...