International Workshop on Secure and Efficient Federated Learning (FL-AsiaCCS)

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. 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. Topics of interest include 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, and Verifiable Federated Learning.