[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:

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

[Submission Instructions]
Each submission can be up to 6 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'22 template (https://www.ijcai.org/authors_kit). 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.

Easychair submission site: https://easychair.org/conferences/?conf=fl-ijcai-22

For enquiries, please email to: fl-ijcai-22@easychair.org

[Publications]
For consideration of a post workshop LNAI publication, the organizing committee will invite a subset of accepted workshop papers to be extended and re-reviewed. More information regarding publications will be released at a later date.

[Organizing Committee]
Steering Chair:
- Qiang Yang (The Hong Kong University of Science and Technology / WeBank, China)
General Co-Chairs:
- Boi Faltings (EPFL, Switzerland)
- Randy Goebel (University of Alberta, Canada)
Program Co-Chairs:
- Han Yu (Nanyang Technological University, Singapore)
- Lixin Fan (WeBank, China)
- Zehui Xiong (Singapore University of Technology and Design, Singapore)
Local Arrangement Chair:
- Guodong Long (University of Technology Sydney, Australia)
Publicity Co-Chairs:
- Le Zhang (University of Electronic Science and Technology, China)
- Sin G. Teo (Institute for Infocomm Research, Singapore)
- Zengxiang Li (Digital Research Institute, ENN Group, China)

[Program Committee]
- Alysa Ziying Tan (Alibaba-NTU Singapore Joint Research Institute)
- Andreas Holzinger (University of Natural Resources and Life Sciences)
- Anran Li (University of Science and Technology of China)
- Bing Luo (City University of Hong Kong, Shenzhen)
- Dimitrios Papadopoulos (Hong Kong University of Science and Technology)
- Grigory Malinovsky (King Abdullah University of Science and Technology)
- Hongyi Peng (Alibaba-NTU Singapore Joint Research Institute)
- Jiangtian Nie (Nanyang Technological University)
- Jiankai Sun (The Ohio State University)
- Jianshu Weng (Swiss Re)
- Jianyu Wang (Carnegie Mellon University)
- Jiawen Kang (Guangdong University of Technology)
- Jihong Park (Deakin University)
- Jinhyun So (University of Southern California)
- Junxue Zhang (Clustar)
- Kallista (Kaylee) Bonawitz (Google)
- Kevin Hsieh (Microsoft Research)
- Mehrdad Mahdavi (Pennsylvania State University)
- Mingyue Ji (University of Utah)
- Peng Zhang (Guangzhou University)
- Philipp Slusallek (Saarland University)
- Praneeth Vepakomma (Massachusetts Institute of Technology)
- Rui Liu (Nanyang Technological University)
- Rui-Xiao Zhang (Tsinghua University)
- Shiqiang Wang (IBM)
- Siwei Feng (Soochow University)
- Songze Li (Hong Kong University of Science and Technology)
- Stefan Wrobel (University of Bonn)
- Theodoros Salonidis (IBM)
- Wei Yang Bryan Lim (Alibaba-NTU Singapore Joint Research Institute)
- Xiaohu Wu (Aalto University)
- Xiaoli Tang (Nanyang Technological university)
- Xu Guo (Nanyang Technological University)
- Yanci Zhang (Nanyang Technological University)
- Yiqiang Chen (Chinese Academy of Sciences)
- Yang Liu (Tsinghua University)
- Yuan Liu (Northeastern University)
- Yuang Jiang (Yale University)
- Yuxin Shi (Alibaba-NTU Singapore Joint Research Institute)
- Zelei Liu (Nanyang Technological university)
- Zhuan Shi (University of Science and Technology of China)
- Zichen Chen (University of California, Santa Barbara)