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 and (Pre-) Aggregation Functions: 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.
Organizers
Cedric Marco-Detchart – Universitat Politècnica de València (Spain)
Jaime A. Rincon Arango – 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)
Francisco Enguix Andrés – Universitat Politècnica de València (Spain)
Submission
See submission instructions for the conference at https://ideal2024.webs.upv.es/submission/
Special Session Papers Submission Deadline: July 26, 2024