Federated Learning for Enterprise AI

Our project explores the design and development of a federated learning platform tailored for enterprise environments. Unlike traditional centralized AI systems, federated learning allows models to be trained across multiple devices or organizations without ever sharing raw data. This innovative approach enhances data privacy and supports compliance with critical regulations like GDPR, HIPAA, and ISO 27001. We developed a scalable and secure system that enables employees across large organizations to collaboratively train machine learning models while keeping their data local. Built with C#, TensorFlow, and deployed on Microsoft Azure, our solution leverages a modular microservices architecture, supports automatic scaling, and integrates privacy-preserving techniques such as Secure Multi-Party Computation (SMPC) and differential privacy.

The platform is designed to balance performance and privacy, making it ideal for sectors that handle sensitive data, such as healthcare and finance. With built-in telemetry and dynamic resource management, it can efficiently support thousands of users without compromising on speed or security. This federated learning framework opens the door to collaborative AI development in enterprises—offering a robust alternative to conventional methods by prioritizing both innovation and trust.