Federated & Distributed Learning Security

This lesson explores the security challenges and protections associated with federated and distributed learning environments. Learners will examine how AI models can be trained across multiple devices or organizations without centralizing sensitive data, while understanding the risks introduced by decentralized architectures. The lesson covers federated learning attack surfaces, secure aggregation, homomorphic encryption, device trust, confidentiality controls, governance considerations, and security-by-design practices that help organizations build secure and trustworthy distributed AI ecosystems.