• Best Paper Award: Ljubomir Rokvic, Panayiotis Danassis and Boi Faltings. Privacy-Preserving Data Quality Evaluation in Federated Learning Using Influence Approximation
• Best Student Paper Award: Yuchen Liu, Chen Chen and Lingjuan Lyu. Exploit Gradient Skewness to Circumvent Defenses for Federated Learning
• Best Student Paper Award: Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Olivia Wiles and Borja Balle. Differentially Private Diffusion Models Generate Useful Synthetic Images

1. Yuchen Liu, Chen Chen and Lingjuan Lyu. Exploit Gradient Skewness to Circumvent Defenses for Federated Learning
2. Hao Sun, Xiaoli Tang, Chengyi Yang, Zhenpeng Yu, Xiuli Wang, Qijie Ding, Zengxiang Li and Han Yu. Hierarchical Federated Learning Incentivization for Gas Usage Estimation
3. Weiming Zhuang and Lingjuan Lyu. Is Batch Normalization Indispensable for Multi-domain Federated Learning?
4. Ziyao Ren, Yan Kang, Lixin Fan, Linghua Yang, Yongxin Tong and Qiang Yang. SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
5. Jiehuang Zhang and Han Yu. A Design Methodology for Incorporating Privacy Preservation into AI Systems
6. Peng Lan, Donglai Chen, Chong Xie, Keshu Chen, Jinyuan He, Juntao Zhang, Yonghong Chen and Yan Xu. Elastically-Constrained Meta-Learner for Federated Learning
7. Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Olivia Wiles and Borja Balle. Differentially Private Diffusion Models Generate Useful Synthetic Images
8. Ljubomir Rokvic, Panayiotis Danassis and Boi Faltings. Privacy-Preserving Data Quality Evaluation in Federated Learning Using Influence Approximation
9. Zheng Wang, Xiaoliang Fan, Zhaopeng Peng, Xueheng Li, Ziqi Yang, Mingkuan Feng, Zhicheng Yang, Xiao Liu and Cheng Wang. FLGO: A Fully Customizable Federated Learning Platform
10. Tianchen Zhou, Zhanyi Hu, Bingzhe Wu and Cen Chen. SLPerf: a Unified Framework for Benchmarking Split Learning
11. Jin Xie, Chenqing Zhu and Songze Li. FedMeS: Personalized Federated Continual Learning Leveraging Local Memory
12. Zhihao Hao, Guancheng Wang, Chunwei Tian and Bob Zhang. A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
13. Yiqiang Chen, Teng Zhang, Xinlong Jiang, Qian Chen, Chenlong Gao and Wuliang Huang. FedBone: Towards Large-Scale Federated Multi-Task Learning
14. Gaolei Li, Jun Wu, Jianhua Li, Yuanyuan Zhao and Longfei Zheng. DSBP: Data-free and Swift Backdoor Purification for Trustworthy Federated Learning via Multi-teacher Adversarial Distillation
15. Wenjie Li, Qiaolin Xia, Hao Cheng, Kouying Xue and Shu-Tao Xia. Vertical Semi-Federated Learning for Efficient Online Advertising
16. Mingxuan Fan, Yilun Jin, Liu Yang, Zhenghang Ren and Kai Chen. VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
17. Kangning Yin, Zhen Ding, Zhihua Dong, Dongsheng Chen, Jie Fu, Xinhui Ji, Guangqiang Yin Yin and Zhiguo Wang. NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data
18. Quentin Pajon, Swan Serre, Hugo Wissocq, Léo Rabaud, Siba Haidar and Antoun Yaacoub. Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures
19. Fubao Zhu, Yanhui Tian, Chuang Han, Yanting Li, Jiaofen Nan, Ni Yao and Weihua Zhou. MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

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.

There are two options for post workshop publications. Authors who wish to submit extended version of their papers elsewhere can opt out of both of these options.

		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.
		Alternatively, selected high quality papers will be invited to submit a journal version of the paper to the Journal of Computer Science & Technology (JCST), Springer. More information will be provided at a later time.

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)