Federated Learning in Artificial Intelligence (AFIA)

At a time when the use of intelligent systems to generate different types of data-based models is on the rise, Federated Learning aims to enable the creation of these systems without compromising the privacy of user data. This piece, within the framework of Trustworthy AI, must be able to build global models based on partial models created by participants, without knowing the specific data of each. Among the issues addressed by these techniques are, among others, the aggregation of individual models, the possible different distribution of data in each of the participating agents, security, and communication frequency.

The objective of the workshop is twofold: on the one hand, to delve into the latest developments in the field of Federated Learning, and on the other, to be a meeting point for the Spanish research community in this area.

Organizers

• Asier Urío Larrea, Public University of Navarre. Email: asier.urio@unavarra.es
• Cedric Marco Detchart, Public University of Navarre. Email: cedric.marco@unavarra.es
• Jaime Andrés Rincón Arangu, University of Burgos. Email: jarincon@ubu.es

Topics of interest

• Federated learning
• Model aggregation techniques in federated learning
• Privacy preservation techniques in federated learning
• Data distribution in federated environments
• Personalized federated learning
• Communication in federated learning systems
• Centralized federated learning systems
• Decentralized federated learning systems
• Security in federated learning systems

The submission process is managed through EasyChair.