This practical workshop will provide an applied overview of how Edge Computing and Federated Learning can transform the way organizations process, analyze, and share data in a more efficient, secure, and privacy-conscious manner. Participants will explore the paradigm shift from centralized models to distributed architectures, understanding the role of Edge Computing in processing data close to where it is generated in order to reduce latency and improve responsiveness, and Federated Learning as a mechanism for training artificial intelligence models without the need to centralize information. Key aspects for their real-world adoption will also be addressed: collaboration between organizations without sharing sensitive data, cybersecurity, model governance, operational complexity, regulatory compliance, and energy impact. The workshop will include real examples and use cases in sectors such as industry, healthcare, and finance. No prior knowledge is required.

OBJECTIVES

  • Understand the role of Edge Computing and Federated Learning in distributed data and artificial intelligence ecosystems.
  • Explore how these technologies enable new models of collaboration between companies and organizations without compromising data privacy.
  • Analyze the business benefits of these approaches, such as cost reduction, improved operational continuity, and real-time decision-making.
  • Identify the main challenges to their adoption: cybersecurity, governance, operational complexity, and energy impact.
  • Learn about practical use cases applicable to sectors such as industry, healthcare, and finance.