Security and Resilience in Distributed Machine Learning

Dr. Kai Li received the B.E. degree from Shandong University, China, in 2009, the M.S. degree from the Hong Kong University of Science and Technology, Hong Kong, in 2010, and the Ph.D. degree in computer science from the University of New South Wales, Sydney, NSW, Australia, in 2014. In 2025, Dr. Li was a Visiting Scholar in the Department of Electrical and Computer Engineering, College of Engineering, Carnegie Mellon University (CMU) at Pittsburgh, Pennsylvania, funded by the CMU-Portugal Visiting Faculty and Researchers Program. From 2024 to 2025, he was a Visiting Research Scholar with the School of Electrical Engineering and Computer Science, TU Berlin, Germany. From 2016 to 2025, he served as a Senior Research Scientist at the CISTER Research Centre, Porto, Portugal, and concurrently as a CMU-Portugal Research Fellow, jointly supported by CMU, USA and the Foundation for Science and Technology (FCT), Lisbon, Portugal. From 2023 to 2024, he was a Visiting Research Scientist with the Division of Electrical Engineering, Department of Engineering, University of Cambridge, UK. In 2022, he was a Visiting Research Scholar with the CyLab Security and Privacy Institute at CMU.Dr. Xin Yuan received the B.E. degree from the Taiyuan University of Technology, Shanxi, China, in 2013, and the dual Ph.D. degree from the University of Technology Sydney (UTS), Sydney, Australia, and the Beijing University of Posts and Telecommunications, Beijing, China, in 2019 and 2020, respectively. She is currently a senior research scientist with CSIRO, Sydney, NSW, Australia. She is also an adjunct senior lecturer at the University of New South Wales since 2023. Her research interests include image and signal processing, data analysis, and cyber-physical security using artificial intelligence (AI) and machine learning (ML) techniques and their applications to the integrity, efficiency, and security of intelligent systems and networks. Dr. Yuan has served as the editor of IEEE Transactions on Vehicular Technology since 2023. She served as a PC member for the ACM Conference on Computer and Communications Security (CCS) 2024. Professor Wei Ni received the B.E. and Ph.D. degrees in Electronics Engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. He is the Associate Dean (Research) in the School of Engineering, Edith Cowan University, Perth, an Adjunct Professor in the University of Technology Sydney, and a Technical Committee Member at Standards Australia. He was a Deputy Project Manager at Alcatel/Alcatel-Lucent Bell Labs from 2005 to 2008; Senior Research Engineer at Nokia from 2008 to 2009; Senior Principal Research Scientist and Group Leader at the Commonwealth Scientific and Industrial Research Organisation from 2009 to 2025; and Conjoint Professor at the University of New South Wales from 2022 to 2026. His research interests include distributed and trusted learning with constrained resources, quantum Internet, and their applications to system efficiency, integrity, and resilience. He was a co-recipient of the ACM Conference on Computer and Communications Security (CCS) 2025 Distinguished Paper Award, and four Best Paper Awards. He has been an Editor for IEEE Transactions on Wireless Communications since 2018, IEEE Transactions on Vehicular Technology since 2022, IEEE Transactions on Information Forensics and Security and IEEE Communication Surveys and Tutorials since 2024, and IEEE Transactions on Network Science and Engineering and IEEE Transactions on Cloud Computing since 2025. He was the Chair of the IEEE VTS NSW Chapter (2020 -- 2022), Track Chair for VTC-Spring 2017, Track Co-chair for IEEE VTC-Spring 2016, Publication Chair for BodyNet 2015, and Student Travel Grant Chair for WPMC 2014. He is a Fellow of IEEE.

• Part 1: Foundations of federated learning and its challenges.- Introduction to federated learning.- Threat landscape in federated learning.- Resilience and security of graph-based federated learning.- Part 2: Adversarial attacks on federated learning.- Model poisoning via variational graph representations.- Biasing federated learning based on adversarial graph attention networks.- Data-agnostic MP Techniques.- Part 3: Defense mechanisms in federated learning.- Privacy-aware wireless federated learning.- Exploring visual explanations for attack detection.- Privacy-utility trade-off in federated learning.- Part 4: Emerging trends and future directions.- Towards fully explainable federated learning.- Sustainability in federated learning.- Summary and outlook.