• 25 - 29 August 2025
  • Ha Noi, Vietnam
in conjunction with ACM AsiaCCS 2025
Workshop on Secure and Efficient Federated Learning
The Workshop on Secure and Efficient Federated Learning aims to provide a platform for discussing the key promises of federated learning and how they can be addressed simultaneously. Given the growing concern over data leakage in modern distributed systems and the requirement of training large-scaled models with limited resources, the security and efficiency of federated learning is the central focus of this workshop.
Call for papers
Since its inception in 2016, Federated Learning (FL) has become a popular framework for collaboratively training machine learning models across multiple devices, while ensuring that user data remains on the devices to enhance privacy. With the exponential growth of data and the increasing diversity of data types, coupled with the limited availability of computational resources, improving the efficiency of training processes in FL is even more urgent than before. This challenge is further amplified by the rise in popularity of training and fine-tuning large-scale models, such as Large Language Models (LLMs), which demand significant computational power. In addition, as FL is now being deployed in more complex and heterogeneous environments, it is more pressing to strengthen security and ensure data privacy in FL to maintain user trust. This workshop aims to bring together academics and industry experts to discuss the future directions of federated learning research, along with practical setups and promising extensions of baseline approaches, with a special focus on how to enhance both the training efficiency and the security in FL. By dealing with these critical issues, we aim to pave the way for more sustainable and secure FL implementations that can effectively handle the requirements of modern AI applications. Papers in double-blind ACM format (up to six pages) can be submitted via EDAS.

Topics of interest include, but are not limited to:

  • Federated Learning in Heterogeneous Networks
  • Communication Efficiency in Federated Learning
  • Scalable and Robust Federated Learning
  • Federated Learning of Large Language Models
  • Verifiable Federated Learning
  • Coded Federated Learning
  • Privacy-Preserving Techniques for Federated Learning
  • Security Attacks and Defenses in Federated Learning
  • Trusted Execution Environments for Federated Learning
Important dates
21 February 2025
21 February 2025
Submission Deadline
8 April 2025
8 April 2025
Workshop Paper Notification
28 April 2025
28 April 2025
Camera Ready Deadline
26 August 2025
26 August 2025
Workshop Date
Submission guidelines

We invite submissions of original research papers, case studies, and position papers related to the workshop’s themes. Submissions should follow the latest ACM Sigconf style conference format and will undergo a double-blind review process. All submissions should be anonymized appropriately. Author names and affiliations should not appear in the paper. The authors should avoid obvious self-references and should appropriately blind them if used. The list of authors cannot be changed (but the order can be) after the submission is made unless approved by the Program Chairs. Submissions must not substantially overlap with papers that are published or simultaneously submitted to other venues (including journals or conferences/workshops). Double-submission will result in immediate rejection. We may report detected violations to other conference chairs and journal editors. The length of paper is limited by 6 pages, including all text, figures, and references.

Submit paper
Workshop chairs
Professor Huaxiong Wang
Nanyang Technological University, Singapore
Huaxiong Wang received a Ph.D. in Mathematics from University of Haifa, Israel in 1996 and a Ph.D. in Computer Science from University of Wollongong, Australia in 2001. He has been with Nanyang Technological University in Singapore since 2006, where he is a Professor in the Division of Mathematical Sciences. Currently he is also the Co-Director of National Centre for Research in Digital Trust and the Deputy Director of Strategic Centre for Research in Privacy-Preserving Technologies and Systems at NTU. Prior to NTU, he held faculty positions at Macquarie University and University of Wollongong in Australia, and visiting positions at ENS de Lyon in France, City University of Hong Kong, National University of Singapore and Kobe University in Japan. His research interest is in cryptography and cybersecurity.
Professor Mikael Skoglund
KTH Royal Institute of Technology, Sweden
Mikael Skoglund received the Ph.D. degree from Chalmers University of Technology, Sweden, in 1997. In 1997, he joined the Royal Institute of Technology (KTH), Stockholm, Sweden, where he was appointed to the Chair in Communication Theory in 2003. At KTH, he heads the Division of Information Science and Engineering and the Department of Intelligent Systems. He has worked on problems in source-channel coding, coding and transmission for wireless communications, Shannon theory, information-theoretic security, information theory for statistics and learning, information and control, and signal processing. He has authored and coauthored around 200 journals and more than 420 conference papers.
Dr. Stanislav Kruglik
Nanyang Technological University, Singapore
Stanislav Kruglik received his bachelor's and master's degrees (Hons.) in applied mathematics and physics from the Moscow Institute of Physics and Technology (MIPT), Moscow, Russia, in 2015 and 2017, respectively, and a master's degree (Hons.) in data science from the Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia, in 2017. He was awarded a Ph.D. in computer science from MIPT in February 2022. Since April 2022, he has been a Research Fellow at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. His research interests include information theory and its applications, particularly in areas related to data storage and security.
Program committee members:

● Huaxiong Wang (Nanyang Technological University)

● Mikael Skoglund (KTH Royal Institute of Technology)

● Deniz Gunduz (Imperial College, London)

● Willy Susilo (University of Wollongong)

● Christopher G. Brinton (Purdue University)

● Han Yu (Nanyang Technological University)

● Antonia Wachter-Zeh (Technical University of Munich)

● Jingge Zhu (University of Melbourne)

● Songze Li (Southeast University)

● Salim El Rouayheb (Rutgers University)

● Son Hoang Dau (RMIT University)

● Harshan Jagadeesh (Indian Institute of Technology Delhi)

● Liang Feng Zhang (ShanghaiTech University)

● Li-Ping Wang (Institute of Information Engineering, Chinese Academy of Sciences)

● Ragnar Thobaben (KTH Royal Institute of Technology)

● Ming Xiao (KTH Royal Institute of Technology)

● Mingzhe Chen (University of Miami)

● Samuel Horvath (Mohamed bin Zayed University of Artificial Intelligence)

● Pasin Manurangsi (Google Research, Thailand)

● Lun Wang (Google, USA)

● Yan Gao (Flower Labs)

● Heng Pan (Flower Labs)

Organizing committee members:

● Stanislav Kruglik (Nanyang Technological University)

● Chengxi Li (KTH Royal Institute of Technology)

● Rawad Bitar (Technical University of Munich)

If you have any questions, please contact:
asiaccsfl@gmail.com
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