Federated Learning is transforming the way intelligent systems are built. Instead of moving sensitive data into a central server, the learning process is pushed out to the devices and institutions that already hold the data. This session introduces Federated Learning Management as a practical discipline that supports this shift toward distributed, privacy aware AI.

• See why federated learning is rising fast as data privacy rules tighten and organizations can no longer rely on centralizing sensitive information. • Follow a clear walkthrough of how federated learning works in practice. Attendees learn how devices train locally, how updates are aggregated, and why secure communication keeps the process trustworthy. • Understand how wildly different client datasets shape model behavior. The talk explains how non IID data affects accuracy, fairness, and convergence, and why managing this variability is central to real deployments. • Get a picture of real system design. The session covers containerized clients, orchestration servers, secure aggregation, and evaluation techniques that function when data never leaves its source. • Learn from real examples in healthcare, finance, and mobile ecosystems. These cases show where federated learning excels and where it needs careful tuning.