About MegaData FL Summer School

The 3rd edition of MegaData Summer School is still the first-ever summer school dedicated to Federated Learning (FL), offering a unique opportunity to dive into one of the most transformative areas of modern artificial intelligence. As real-world applications increasingly demand privacy-preserving, decentralized machine learning, FL has emerged as a revolutionary approach that enables AI models to be trained collaboratively across distributed devices while ensuring that sensitive data remains local.

With the rapid rise of autonomous systems, smart cities, healthcare technologies, and industrial IoT, traditional centralized AI models face growing latency, bandwidth constraints, and data security challenges. FL addresses these challenges by shifting computation to the edge, allowing models to be trained efficiently and securely in diverse environments. However, deploying FL in real-world settings is highly complex due to heterogeneous data distributions, varying device capabilities, and resource constraints.

This summer school will provide participants with a strong foundation in FL, covering core concepts, system architectures, optimization techniques, and emerging industry applications. As the first event of its kind, the program is designed for MSc and PhD students, researchers, and professionals interested in edge intelligence, distributed systems, and privacy-preserving AI.

Through theoretical lectures, hands-on coding sessions, and project work, participants will gain practical skills for designing and deploying FL models in real-world applications. Hosted on-site in Santiago de Compostela, Spain, the summer school features morning lectures exploring FL principles and the latest advancements, followed by afternoon hands-on sessions and collaborative projects, allowing attendees to apply their knowledge in practice.

As FL continues to reshape the future of AI and distributed computing, this pioneering summer school will equip participants with cutting-edge expertise in a rapidly growing field with increasing demand in both academia and industry.