Federated learning is reshaping how organisations train machine learning models on sensitive data without ever centralising it. In this episode, Lucas and Luna break down a real-world case: how a consortium of six European hospitals used federated learning to train a diagnostic model for rare paediatric cancers — achieving accuracy comparable to a centralised model while keeping each hospital's patient data behind its own firewall. They walk through the technical architecture: the role of a coordination server, how model updates are aggregated using FedAvg, and what happens when non-IID data distributions cause client drift. Luna pushes back on the communication cost argument, and Lucas explains how compression techniques and asynchronous updates are making federated learning practical at scale. They also touch on the regulatory angle — why GDPR and HIPAA are driving adoption faster than any technical breakthrough. Whether you're a data scientist evaluating privacy-preserving ML or just curious how Apple trains Siri without reading your keystrokes, this episode gives you the concrete mechanics behind a paradigm shift in distributed machine learning.

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