Welcome!

The FIDTA 2025 workshop seeks to create a unique forum focused on the fusion of Federated Learning (FL) and Digital Twin (DT) technologies within distributed, dynamic, and resource-constrained networked systems. In an era where mobile, IoT, and cyber-physical systems generate massive, decentralized data streams, there is an urgent need for privacy-preserving, real-time, and scalable intelligence without relying on centralized data collection. Federated Intelligence — the combination of federated learning, decentralized AI, and collaborative optimization — offers a promising paradigm for training robust AI models directly at the network edge or across autonomous devices. Meanwhile, Digital Twin frameworks provide live, evolving digital representations of physical entities and systems, enabling predictive analytics, anomaly detection, and decision-making in complex, dynamic environments.

Workshop Topics
FIDTA aims to spark interdisciplinary collaborations, foster innovation, and accelerate the deployment of trustworthy, resilient, and intelligent networked systems for the next generation of mobile and distributed environments. We focus on all aspects of edge network, digital twin and federated technologies, including but not limited to the following:

-Federated learning algorithms for dynamic IoT and autonomous systems
-Digital twin generation and maintenance in mobile and distributed networks
-Communication-efficient and energy-aware federated intelligence
-Privacy, trust, and security challenges in federated twin systems
-Edge intelligence and decentralized digital twin ecosystems
-Applications: smart cities, autonomous vehicles, swarm robotics, industrial IoT
-Distributed inference and online learning for cyber-physical networks
-Benchmarking and evaluation frameworks for federated twin applications

Important Dates
Workshop Paper Submissions: July 30, 2025
Notification of acceptance: August 15, 2025
Camera-ready Workshop Papers: August 30, 2025
Workshop Dates: October 27-30, 2025

TPC and General Chairs
Francesco Piccialli (University of Naples Federico II, Italy)
David Camacho (Universidad Politécnica de Madrid, Spain)
Daniela Annunziata (University of Naples Federico II, Italy)
Liu Wang (Hong Kong Polytechnic University, China)