The Workshop on Federated Learning for Autonomous and Connected Vehicles is a specialized academic–industry forum that brings together researchers, automotive engineers, data scientists, and cybersecurity experts to explore federated learning (FL) as a privacy-preserving, decentralized learning paradigm for next–generation mobility systems. It focuses on bridging FL algorithms, edge computing, vehicle-to-everything (V2X) communications, and autonomous driving tasks, including environmental perception, trajectory prediction, collaborative mapping, and intelligent decision-making, while addressing critical challenges such as on–device data heterogeneity, limited communication bandwidth, real–time inference latency, and strict automotive data privacy and safety regulations. Through technical talks, panel discussions, and demo presentations, the workshop fosters interdisciplinary collaboration to advance robust, scalable, and secure FL frameworks that enable connected vehicles to collectively learn without sharing raw sensor or user data, accelerating the reliable deployment of autonomous driving while complying with global data governance standards.
Please follow the submission guideline from the VTC2026-Fall Submission Website.
Please submit the paper to https://vtc2026fall.trackchair.com/
Momina Shaheen, University of Roehampton, UK (momina.shaheen@roehampton.ac.uk)
Dr. Momina Shaheen is an academic and researcher in computer science with over 8 years of experience in teaching and research. She currently serves as a Senior Lecturer in Computing and Programme Leader at the University of Roehampton London. Her work focuses on edge computing, the Internet of Things, federated learning, and cybersecurity, with applications across smart cities, healthcare, finance, and education. Shaheen holds a B.Sc. in Information Technology, an M.Eng. in Software Engineering, and defended her Ph.D. in Computer Science, focusing on improving deep learning performance in federated machine learning. Her academic contributions include over 46 peer-reviewed publications in Q1 journals, multiple book chapters, and editorial work. She is also serving as Editor of Robotics and AI Review journal. She has edited 5 books in the field of algorithms and computing, and served as a reviewer for prominent journals such as Nature (under Early Career Reviewer Program), IET Information Security, Springer Scientific Reports, MDPI Electronics, IEEE Access, PLOS ONE, and ACM Transactions, and has chaired international conferences.
Weiwei Jiang, Beijing University of Posts and Telecommunications, China (jww@bupt.edu.cn)
Dr. Weiwei Jiang received the B.Sc. Degree of Electronic Engineering and Ph.D. Degree of Information and Communication Engineering from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2013 and 2018, respectively. He is currently an associate professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, and Key Laboratory of Universal Wireless Communications, Ministry of Education. His current research interests include artificial intelligence, machine learning, big data, wireless communication and edge computing. He has published more than 100 academic papers in IEEE Trans and other journals, with more than 5800 citations in Google Scholar. He is one of 2022, 2023, 2024 and 2025 Stanford's List of World's Top 2% Scientists.
Christo K Thomas, Worcester Polytechnic Institute, Worcester, MA, 01609, USA (cthomas2@wpi.edu)
Dr. Christo K Thomas received his BS in Electronics and Communication Engineering from National Institute of Technology, Calicut, India in year 2010, his MS in Telecommunication Engineering from Indian Institute of Science, Bangalore, India in year 2012, and his PhD from EURECOM, France in year 2020. He is currently an assistant professor at the Electrical and Computer Engineering Department at WPI. Previously, he was a postdoctoral associate at the Electrical and Computer Engineering Department at Virginia Tech. His research interests include semantic communications, integrated sensing and communication, statistical signal processing, and artificial general intelligence (AGI)-native wireless systems. He served as a staff design engineer on 4G LTE at Broadcom (2012–2014), a design engineer at Intel (2014–2017), and a staff engineer on 5G modem development with Qualcomm’s Wireless R&D division in Espoo, Finland (Nov. 2020–June 2022). He received the Best Student Paper Award at IEEE SPAWC 2018 (Kalamata, Greece), earned third prize in the ITU AI/ML in 5G Challenge (2020, NCSU, USA) for the “Learned Chester” ML5G-PHY channel estimation team entry, and was awarded the AI-RAN Alliance Innovation Award in November 2025 for his proposal on emergent semantic communication for AI-RAN.
Milankumar Rana, HeadstormAI, Dallas, Texas, USA (Milan.dgen@gmail.com)
Dr. Milankumar Rana is a Principal AI Architect and Senior Cloud Engineer at HeadstormAI, USA, where he leads enterprise-scale AI transformation initiatives across cloud, MLOps, GenAI, and secure agentic systems. He currently spearheads multiple production deployments including SecureGPT, GenAI Landing Zones, MLOps platforms, and AI Foundry architectures for large enterprises, focusing on zero-trust networking, governance-driven design, and compliant AI systems on Microsoft Azure and hybrid environments. He also serves as Chair of the ACM Dallas Chapter and is actively involved in the global research community as a technical program committee member and reviewer for several international AI and computer science conferences. His professional interests span agentic AI architectures, Model Context Protocol (MCP), AIOps, DevSecOps, cloud security, memorycentric AI systems, and enterprise observability. He is the author of multiple technical chapters and is currently preparing books and research works on AI-powered automation, MCP server architectures, and intelligent cloud operations. With extensive industry experience bridging academia and enterprise engineering, Dr. Rana has led large-scale initiatives in AI governance, secure cloud infrastructure, and data-driven supply chain intelligence. His work emphasizes practical, production-grade AI systems with strong compliance alignment (NIST, ISO 27001, SOC2) and real-world impact across healthcare, , and distributed enterprises.
Bagesh Kumar, Manipal University Jaipur, India (bagesh.kumar@jaipur.manipal.edu)
Dr. Bagesh Kumar received the B.Tech. degree from Rustamji Institute of Technology (RJIT), India, in 2016, and the M.Tech. and Ph.D. degrees in Information Technology from the Indian Institute of Information Technology (IIIT), Allahabad, India, in 2018 and 2023, respectively. He is currently an Assistant Professor with the Department of Information Technology, Manipal University Jaipur. His current research interests include artificial intelligence, machine learning, deep learning, generative AI, and autonomous systems (drones and robotics). He has published academic papers in highimpact journals such as Knowledge-Based Systems and various international conferences, with nearly 100 citations in Google Scholar. He has contributed to research projects funded by the Department of Science and Technology (DST), India, and has collaborated with international institutions such as THM University, Germany.
Suresh Chavhan, Indian Institute of Science Education and Research Thiruvananthapuram, India (suresh@iisertvm.ac.in)
Prof. Suresh Chavhan received the B.E. Degree in Electrical and Electronics Engineering from VTU Belgaum, Karnataka, in 2011. M.Tech. Degree in System Analysis and Computer Applications, NITK Surathkal, Karnataka, in 2013, and Ph.D. Degree in Electrical Communication Engineering from the Indian Institute of Science, Bengaluru, in 2019, India. He is currently an assistant professor with the School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram, Ministry of Education, India. His current research interests include artificial intelligence, machine learning, big data, wireless communication, and edge computing. He has published more than 65 academic papers in IEEE Transactions and other journals, with over 1,000 citations on Google Scholar. He has received the 2021 IEEE Systems Journal Best Paper Award.