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:

Techniques: 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

Applications: 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.

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: https://easychair.org/conferences/?conf=fl-ijcai-23

For enquiries, please email to: fl-ijcai-23@easychair.org

• Steering Chair:
• Qiang Yang (The Hong Kong University of Science and Technology / WeBank, China)
• General Co-Chairs:
• Han Yu (Nanyang Technological University, Singapore)
• Lixin Fan (WeBank, China)
• Program Co-Chairs:
• Bo Li (University of Illinois at Urbana-Champaign, USA)
• Guodong Long (University of Technology Sydney, Australia)
• Local Arrangement Co-Chairs:
• Yang Liu (Tsinghua University, China)
• Le Zhang (University of Electronic Science and Technology, China)
• Publicity Co-Chairs:
• Sin G. Teo (Institute for Infocomm Research, A*STAR, Singapore)
• Chao Jin (Institute for Infocomm Research, A*STAR, Singapore)