Federated Learning, Intelligent Fusion and Applications (FLIFA)

Abstract

The fast-growing fields of Federated Learning and Intelligent Systems are changing how collaborative and decentralized machine learning is approached, promoting privacy and efficiency in diverse applications. This special session will explore the integration of Federated Learning with advanced Intelligent Systems, including cognitive assistants, data fusion, and (pre-) aggregation functions. The session will address complex challenges in areas such as classification, robotics, healthcare, and environmental management. The goal is to provide a platform for presenting innovative research and practical implementations that enhance data privacy and improve decision-making capabilities in Intelligent Systems.

Topics

Reflecting the interdisciplinary nature of Federated Learning and Intelligent Systems, this session will cover a broad spectrum of topics, including but not limited to:

● Federated Learning: Exploring concepts, theory, and applications with a focus on privacy-preserving techniques.
● Cognitive Assistants: Development and deployment of AI-powered assistants in various sectors such as healthcare and smart environments.
● Data Fusion, (Pre-) Aggregation Functions and Inspired Aggregation: Methods for integrating diverse data sources to enhance understanding and decision-making in complex situations and studying aggregation operators and their role in improving
system quality and performance.
● Intelligent Systems in Healthcare and Robotics: Applying Intelligent Systems to manage healthcare data and automate robotics.
● Precision Agriculture and Environmental Monitoring: Using Intelligent Systems to improve agricultural practices and monitor environmental changes effectively.
● Digital Twins and AI Decision-making: Using FL to create digital twins and support frameworks for explainable AI.
● Disaster Response and Swarm Intelligence: Implementing Intelligent Systems for effective disaster management and exploring group behaviour in multi-agent environments.
● Machine Learning applications: Studies involving the use of AI and ML approaches in applied scenarios.
● Cybersecurity and AI-Powered Threat Detection in IoT Devices.

This session aims to bridge the gap between theoretical research and practical applications, providing insights into the future of intelligent systems and their impacts on society.

Organizers

-Cedric Marco-Detchart – cedric.marco@unavarra.es – Universidad Pública de Navarra (Spain)
-Jaime A. Rincon Arango – jarincon@ubu.es – Universidad de Burgos (Spain)
-Vicente Julian – Universitat Politècnica de València (Spain)
-Carlos Carrascosa Casamayor – Universitat Politècnica de València (Spain)
-Paulo Novais – Universidade do Minho (Portugal)
-Carlos Lopez-Molina – Universidad Pública de Navarra (Spain)
-Laura de Miguel Turullols – Universidad Pública de Navarra (Spain)
-Giancarlo Lucca – Universidade Católica de Pelotas (Brazil)
-Graçaliz P. Dimuro – Universidade Federal do Rio Grande (Brazil)

-Jose Guillermo Guarnizo Marin – Universidad Distrital Francisco Jose de Caldas (Colombia)
-Humberto Bustince – Universidad Pública de Navarra (Spain)

Submission

See submission instructions for the conference at the call for papers. At the beginning of the submission, please choose the track “Federated Learning, Intelligent Fusion and Applications (FLIFA)”.

Special Session Papers Submission Deadline: June 15th, 2025