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Edge Computing y Federated Learning: Inteligencia Distribuida para el Negocio

General information

This practical workshop will offer an applied overview of how Edge Computing and Federated Learning can transform the way organisations process, analyse and share data in a more efficient, secure and privacy-respecting manner. Participants will explore the paradigm shift from centralised models to distributed architectures, understanding the role of Edge Computing in processing data close to where it is generated to reduce latency and improve responsiveness, and of Federated Learning as a mechanism to train artificial intelligence models without the need to centralise information. Key aspects for their real-world adoption will also be addressed: collaboration between organisations without sharing sensitive data, cybersecurity, model governance, operational complexity, regulatory compliance and energy impact. The workshop will include real examples and case studies in sectors such as industry, healthcare or 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 businesses and organisations without compromising data privacy.
  • Analyse the business benefits of these approaches, such as cost reduction, improved operational continuity and real-time decision-making.
  • Identify the main challenges for their adoption: cybersecurity, governance, operational complexity and energy impact.
  • Learn about practical case studies applicable in sectors such as industry, healthcare or finance.
Register now!
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Price: 

Free

graduation
Modality: 
Online
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Language:
Spanish