How to Deploy Federated Learning Systems in Multi-Tenant SaaS Environments
How to Deploy Federated Learning Systems in Multi-Tenant SaaS Environments In a world increasingly focused on data privacy, federated learning has emerged as a powerful technique to train machine learning models without centralizing sensitive data. This approach is especially relevant in multi-tenant SaaS platforms, where data from different clients must remain strictly isolated yet contribute to smarter shared models. This guide explains how to design, secure, and scale federated learning systems across tenant boundaries while ensuring compliance and performance. Table of Contents Why Federated Learning in SaaS Matters Core Architecture for Federated Learning Data Isolation and Privacy Techniques Recommended Frameworks and Platforms Deployment Best Practices Why Federated Learning in SaaS Matters SaaS platforms often serve clients across sectors like healthcare, finance, and legal — industries that deal with highly sensitive data. Traditional ma...