Workshop FLEdge-AI @ ACM MOBICOM 2025

Organizers and Chairs
Francesco Piccialli (University of Naples Federico II, Italy)
David Camacho (Universidad Politécnica de Madrid, Spain)
Fabio Giampaolo (University of Naples Federico II, Italy)
Jon Crowcroft (University of Cambridge, UK)
Liu Wang (Hong Kong Polytechnic University, China)
Yuchao Zhang (Beijing University of Posts and Telecommunications, China)

Official Link: https://edgeai2025.github.io/

Linked Special Issue: https://onlinelibrary.wiley.com/page/journal/14680394/homepage/call-for-papers/si-2025-000707

Aim and Scope

The FLEdge-AI 2025 workshop aims to bring together researchers, practitioners, and industry leaders to explore the critical intersection of Federated Learning (FL), Edge AI, privacy, and mobility. As the mobile computing landscape rapidly evolves towards 6G, pervasive edge intelligence, and decentralized AI, FL and Edge AI are emerging as foundational technologies. They enable privacy-preserving, resilient, and distributed machine learning across dynamic, resource-constrained, and heterogeneous environments. This workshop will serve as a premier forum for discussing the latest research in algorithms, systems, and real-world deployments of federated learning and edge AI in mobile and wireless scenarios.

We aim to address the pressing technical challenges and opportunities in making FL and Edge AI practical and impactful for mobile users and applications. Key issues include communication bottlenecks in mobile systems, device and statistical heterogeneity, user mobility and dynamic network topologies, and ensuring robust privacy and security in open and untrusted environments. The goal is to foster innovations that bridge the gap between theory and deployment, particularly focusing on how these technologies can operate effectively under the constraints of mobile networks and edge devices.

Topics of interest include, but are not limited to, the following:

The goal of this FLEdge-AI 2025 Workshop is to bring together scientists, researchers, and engineers to identify new problems, latest novel topics, and emerging technologies.

We focus on all aspects of edge network, data privacy and federated technologies, including but not limited to the following:

-Federated Learning protocols for mobile, vehicular, and edge networks
-Communication-efficient FL (e.g., quantization, sparsification, gossip-based)
-FL under client mobility, heterogeneity, and intermittent connectivity
-Privacy and security in mobile FL (e.g., differential privacy, secure aggregation)
-Personalization and federated transfer learning
-Multi-agent and swarm intelligence-based FL
-Benchmarking FL in wireless/mobile environments
-Network-aware optimization and system-level co-design for FL
-FL deployment in UAVs, mobile edge clouds, and autonomous systems

Important Dates
Workshop Paper Submissions: July 25, 2025
Notification of acceptance: September 15, 2025
Camera-ready Workshop Papers: October 10, 2025
Workshop Dates: November 8, 2025