International Workshop on Trustworthy Federated Learning
in Conjunction with IJCAI 2023 (FL-IJCAI'23)

Submission Due: April 26, 2023 (23:59:59 AoE)
Notification Due: May 15, 2023 (23:59:59 AoE)
Final Version Due: June 01, 2023 (23:59:59 AoE)

Workshop Date: August 19-21, 2023
Venue: Sheraton Grand Macao Hotel, Macau

Call for Papers

Federated Learning (FL), a learning paradigm that enables collaborative training of machine learning models in which data reside and remain in distributed data silos during the training process. FL is a necessary framework to ensure AI thrive in the privacy-focused regulatory environment. As FL allows self-interested data owners to collaboratively train machine learning models, end-users can become co-creators of AI solutions. To enable open collaboration among FL co-creators and enhance the adoption of the federated learning paradigm, we envision that communities of data owners must self-organize during FL model training based on diverse notions of trustworthy federated learning, which include, but not limited to, security and robustness, privacy-preservation, interpretability, fairness, verifiability, transparency, auditability, incremental aggregation of shared learned models, and creating healthy market mechanisms to enable open dynamic collaboration among data owners under the FL paradigm. This workshop aims to bring together academic researchers and industry practitioners to address open issues in this interdisciplinary research area. For industry participants, we intend to create a forum to communicate problems are practically relevant. For academic participants, we hope to make it easier to become productive in this area. The workshop will focus on the theme of building trustworthiness into federated learning to enable open dynamic collaboration among data owners under the FL paradigm, and make FL solutions readily applicable to solve real-world problems.

Topics of interest include, but are not limited to:
  • Adversarial learning, data poisoning, adversarial examples,
    adversarial robustness, black box attacks
  • Architecture and privacy-preserving learning protocols
  • Auctions in federated learning
  • Auditable federated learning
  • Automated federated learning
  • Explainable federated learning
  • Fairness-aware federated learning
  • Federated learning and distributed privacy-preserving algorithms
  • Federated transfer learning
  • Human-in-the-loop for privacy-aware machine learning
  • Incentive mechanism and game theory for federated learning
  • Interpretable federated learning
  • Model merging and sharing
  • Personalization in federated learning
  • Privacy-aware knowledge driven federated learning
  • Privacy-preserving techniques (secure multi-party computation, homomorphic
    encryption, secret sharing techniques, differential privacy) for machine learning
  • Robustness in federated learning
  • Security for privacy, privacy leakage verification and self-healing etc.
  • Trade-off between privacy, safety, effectiveness and efficiency
  • Transparent federated learning
  • Verifiable federated learning
  • Algorithm auditability
  • Approaches to make GDPR-compliant AI
  • Data value and economics of data federation
  • Open-source frameworks for privacy-preserving distributed learning
  • Safety and security assessment of federated learning
  • Solutions to data security and small-data challenges in industries
  • Standards of data privacy and security

More information on previous workshops can be found here.

Submission Instructions

Each submission can be up to 7 pages of contents plus up to 2 additional pages of references and acknowledgements. The submitted papers must be written in English and in PDF format according to the IJCAI'23 template. All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details. Submission will be accepted via the Easychair submission website.

Based on the requirement from IJCAI'23, at least one author of each accepted paper must travel to the IJCAI venue in person. In addition, multiple submissions of the same paper to more than one IJCAI workshop are forbidden.

Easychair submission site:

For enquiries, please email to:

Post Workshop Publications


Selected high quality papers will be invited for publication as chapters in an edited book in the Lecture Notes in Artificial Intelligence (LNAI) series under Springer. More information will be provided at a later time.

Invited Talks


Title: Federated Learning in Healthcare: Overcoming Data Heterogeneity Challenges

Speaker: Xiaoxiao Li, Assistant Professor, the University of British Columbia (UBC), Vancouver, Canada

Xiaoxiao Li is an Assistant Professor at the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) starting August 2021. In addition, Dr. Li holds positions as a Faculty Member at Vector Institute and an adjunct Assistant Professor at Yale University. Before joining UBC, Dr. Li was a Postdoc Research Fellow at Princeton University. Dr. Li obtained her Ph.D. degree from Yale University in 2020. Dr. Li's research focuses on developing theoretical and practical solutions for enhancing the trustworthiness of AI systems in healthcare. Specifically, her recent research has been dedicated to advancing federated learning techniques and their applications in the medical field. Dr. Li's work has been recognized with numerous publications in top-tier machine learning conferences and journals, including NeurIPS, ICML, ICLR, MICCAI, IPMI, ECCV, TMI, TNNLS, and Medical Image Analysis. Her contributions have been further acknowledged with several best paper awards at prestigious international conferences.

Organizing Committee

Program Committee

  • Alysa Ziying Tan (Alibaba-NTU Singapore Joint Research Institute, Singapore)
  • Anran Li (Nanyang Technological University, Singapore)
  • Dimitrios Papadopoulos (Hong Kong University of Science and Technology, Hong Kong)
  • Hongyi Peng (Nanyang Technological University, Singapore)
  • Huawei Huang (Sun Yat-Sen University, China)
  • Jiangtian Nie (Nanyang Technological University, Singapore)
  • Jiankai Sun (The Ohio State University, USA)
  • Jianshu Weng (Chubb, Singapore)
  • Jihong Park (Deakin University, Australia)
  • Jinhyun So (University of Southern California, USA)
  • Kevin Hsieh (Microsoft Research, USA)
  • Paulo Ferreira (Dell Technologies, USA)
  • Peng Zhang (Guangzhou University, China)
  • Qin Hu (George Washington University, USA)
  • Rui Liu (Nanyang Technological University, Singapore)
  • Shengchao Chen (University of Technology Sydney, Australia)
  • Shiqiang Wang (IBM Thomas J. Watson Research Center, USA)
  • Siwei Feng (Soochow University, China)
  • Songze Li (Hong Kong University of Science and Technology, Hong Kong)
  • Wei Yang Bryan Lim (Nanyang Technological University, Singapore)
  • Wen Wu (Peng Cheng Laboratory, China)
  • Xiaohu Wu (Beijing University of Posts and Telecommunications, China)
  • Xiaoli Tang (Nanyang Technological University, Singapore)
  • Xu Guo (Nanyang Technological University, Singapore)
  • Yanci Zhang (Shandong University, China)
  • Yang Zhang (Nanjing University of Aeronautics and Astronautics, China)
  • Yiqiang Chen (Chinese Academy of Sciences, China)
  • Yuang Jiang (Yale University, USA)
  • Yulan Gao (Nanyang Technological University, Singapore)
  • Yuxin Shi (Nanyang Technological University, Singapore)
  • Zelei Liu (Nanyang Technological University, Singapore)
  • Zhuan Shi (University of Science and Technology of China, China)
  • Zhuowei Wang (CSRIO, Australia)
  • Zichen Chen (University of California, Santa Barbara, USA)

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