1. LoByITFL: Low Communication Secure and Private Federated Learning
   Yue Xia, Maximilian Egger, Christoph Hofmeister and Rawad Bitar (Technical University of Munich, Germany)
2. FedDDF: Dynamic Dataset Filtering in Federated Large Language Model Training
   Nguyen Linh Bao Nguyen (VinUniversity, Vietnam & Swinburne University of Technology, Australia); Thuan Quang Tran (VinUniversity, Vietnam & Ho Chi Minh University of Science, Vietnam); Kok-Seng Wong (VinUniversity, Vietnam)
3. FedKoE: Enhancing Federated Multimodal Learning through Knowledge of Experts
   Duy Khuong and An D Nguyen (VinUniversity, Vietnam); Duy Nguyen (University of Illinois Urbana-Champaign, USA); Kok-Seng Wong (VinUniversity, Vietnam)
4. Coordinate-Wise Median in Byzantine Federated Learning
   Tijana Milentijevic (TU Berlin, Germany); Melanie Cambus (Aalto University, Finland); Darya Melnyk and Stefan Schmid (TU Berlin, Germany)
5. Detect & Score: Privacy-Preserving Misbehavior Detection and Contribution Evaluation in Federated Learning
   Marvin Xhemrishi (Technical University of Munich, Germany); Alexandre Graell i Amat (Chalmers University of Technology, Sweden); Balazs Pejo (BME, Hungary)
6. Detecting Erroneous Classifiers in Batch Distributed Inference
   Yuval Shicht and Yuval Cassuto (Technion, Israel)
7. Efficient Model Propagation for Peer-to-Peer Federated Learning using Minimum Spanning Tree and Gossip Networks
   Alka Luqman (Nanyang Technological University, Singapore)

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.

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.

Topics of interest include, but are not limited to:

• Coded Federated Learning
• Communication Efficiency in Federated Learning
• Federated Learning in Heterogeneous Networks
• Federated Learning of Large Language Models
• Privacy-Preserving Techniques for Federated Learning
• Scalable and Robust Federated Learning
• Security Attacks and Defenses in Federated Learning
• Trusted Execution Environments for Federated Learning
• Verifiable Federated Learning

More information on previous workshops can be found here.

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.

Papers in double-blind ACM format of up to six pages, including all text, figures and references can be submitted via EDAS at https://edas.info/N33095.

For questions, please contact: asiaccsfl@gmail.com

Huaxiong Wang (NTU)

Mikael Skoglund (KTH)

Stanislav Kruglik (NTU)

Organizing Committee Members

Chengxi Li (KTH)

Rawad Bitar (TUM)

Han Yu (NTU)