The 4th Federated Machine Learning International Summer School

Now in its fourth edition, the Federated Machine Learning International Summer School bills itself as the first programme dedicated entirely to Federated Learning (FL) — the privacy-preserving approach that trains AI models collaboratively across distributed devices while keeping raw data local. Held on-site in Paris from 19 to 27 October 2026, it targets MSc and PhD students, researchers and practitioners drawn to edge intelligence, distributed systems and privacy-preserving machine learning.

The format pairs morning lectures on FL principles, system architectures and optimisation with afternoon hands-on coding sessions and collaborative project work, so attendees leave able to design and deploy FL models rather than just discuss them. Dedicated workshops cover the Flower, Scaleout and Indigma frameworks, grounding the theory in production-grade tooling. Guest lecturers are drawn from Flower Labs, OpenMined, NVIDIA Flare, Scaleout and several universities across Europe, giving a balanced academic-and-industry view of a field increasingly central to healthcare, smart cities and industrial IoT. Applications run through the organisers' portal, with acceptance notifications due in September 2026. For anyone wanting an intensive, code-first grounding in decentralised AI, this is a focused and unusually specialised week.