International Workshop on Federated Learning for User Privacy and Data Confidentiality
in Conjunction with IJCAI 2019 (FL-IJCAI'19)

Submission Due: May 26, 2019 (23:59 UTC-12)
Notification Due: June 24, 2019 (23:59 UTC-12)

Workshop Date: August 12, 2019
Venue: Room Sicily 2506, Venetian Macau, Macau

Workshop Program

Time Activity
08:00 – 08:30 Arrival and Registration
08:30 – 09:00 Opening Address by Qiang Yang
09:00 – 09:40 Keynote Speech by Shahrokh Daijavad - Enterprise Context Federated Learning: Challenges and Approaches
09:40 – 10:40 Lightning Talks (5 minutes each, up to 12 talks)
10:40 – 11:00 Tea Break
11:00 – 12:00 Session 1 (4 talks, 15 minutes each)
12:00 – 13:30 Lunch & Poster Session
13:30 – 14:10 Keynote Speech by Jakub Konečný - Federated Learning from Research to Practice
14:10 – 15:10 Session 2 (4 talks, 15 minutes each)
15:10 – 15:30 Tea Break
15:30 – 16:30 Session 3 (4 talks, 15 minutes each)
16:30 -17:00 Panel Discussion (Mediated by Lixin Fan)
  1. Benny Pinkas, Bar-Ilan University
  2. Shahrokh Daijavad, IBM
  3. Richard Tong, Squirrel AI Learning
  4. Jakub Konečný, Google
  5. Baofeng Zhang, Huawei
  6. Junxue Zhang, Clustar
  7. Ji Feng, Sinovation Ventures
17:00 – 17:30 Award Ceremony and Closing


  • Best Theory Paper: Huadi Zheng, Haibo Hu and Han Ziyang. Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning?
  • Best Application Paper: Yiqiang Chen, Jindong Wang and Chaohui Yu. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
  • Best Student Paper: Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian and Kai Chen. Quantifying the Performance of Federated Transfer Learning
  • Best Presentation: Aleksei Triastcyn and Boi Faltings. Federated Generative Privacy

Accepted Papers

  1. Adam Richardson, Aris Filos-Ratsikas and Boi Faltings. Eliciting High-Quality Data via Influence for Linear Regression
  2. Aleksei Triastcyn and Boi Faltings. Federated Generative Privacy
  3. Bo Li, Kele Xu, Haibo Mi, Dawei Feng, Huaimin Wang and Yanbo J. Wang. Multi-task Learning for Vertical Federated Machine Learning: A Case Study For Cross-Lingual Short-Text Matching
  4. Chenghao Hu, Jingyan Jiang and Zhi Wang. Decentralized Federated Learning: A Segmented Gossip Approach
  5. Di Chai, Leye Wang, Kai Chen and Qiang Yang. Secure Federated Matrix Factorization
  6. Eunjeong Jeong, Seungeun Oh, Jihong Park, Hyesung Kim, Mehdi Bennis and Seong-Lyun Kim. Multi-Hop Federated Private Data Augmentation with Sample Compression
  7. Fuyong Zhang, Yi Wang and Kuan Li. Decision-Based Evasion Attacks to Random Forest Ensembles
  8. Guan Wang. Interpret Federated Learning with Shapley Values
  9. Guanyu Lin, Feng Liang, Weike Pan and Zhong Ming. A General Federated Collaborative Rating Prediction Framework for Privacy-Aware Recommendation
  10. Han Cha, Jihong Park, Hyesung Kim, Seong-Lyun Kim and Mehdi Bennis. Federated Reinforcement Distillation with Proxy Experience Memory
  11. Huadi Zheng, Haibo Hu and Han Ziyang. Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning?
  12. Jinming Cui, Huaping Li and Meng Yang. Privacy-Preserving Computation over Genetic Data: HLA Matching and so on
  13. Jiyue Huang, Maoyu Du, Kai Lei and Hongting Zhao. Attention-Based Updates Aggregation in Federated Learning
  14. Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen and Qiang Yang. SecureBoost: A Lossless Federated Learning Framework
  15. Kun Zhao, Haoyuan Zheng, Wei Xi, Zhi Wang, Wei Shi, Liang Lin and Jizhong Zhao. SFL: Secure Federated Learning based on Jaccard Coefficient Threshold
  16. Lei Song, Chunguang Ma and Yun Zhang. Privacy-preserving Transfer Learning
  17. Lingwei Kong, Jianzong Wang, Zhangcheng Huang, Anxun He, Linjie Chen, Man Zhang and Jing Xiao. A Federated Learning Schema with Additive Homomorphic Encrytion
  18. Mengwei Yang, Linqi Song, Jie Xu, Congduan Li and Guozhen Tan. The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
  19. Mingshu Cong, Xi Weng, Han Yu and Zhongming Qu. FML Incentive Mechanism Design: Concepts, Basic Settings, and Taxonomy
  20. Qinbin Li, Bingsheng He and Zeyi Wen. Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
  21. Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian and Kai Chen. Quantifying the Performance of Federated Transfer Learning
  22. Ruihui Zhao, Mizuho Iwaihara, Xiaodong He and Shunnan Xu. A Lightweight Efficient Searchable Encryption Scheme using Supervised Sentence Representations
  23. Shengwen Yang, Bing Ren, Xuhui Zhou and Liping Liu. Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
  24. Shudong Huang, Wei Shi, Zenglin Xu and Ivor Tsang. Iterative Orthogonal Federated Multi-view Learning
  25. Yang Liu, Mixngin Chen, Ruiyuan Li, Yanment Lu, Changbin Lu, Jiandong Gao, Junbo Zhang and Yu Zheng. Federated Learning with Digital Gateway: Methodologies, Tools and Applications
  26. Yang Liu, Xiong Zhang, Shuqi Qin and Xiaoping Lei. Distributed Privacy--Preserving Iterative Summation Protocols
  27. Yang Liu, Yan Kang, Han Yu, Tianjian Chen and Qiang Yang. Secure Federated Transfer Learning
  28. Yexuan Shi, Zhiyang Su, Di Jiang, Zimu Zhou, Wenbin Zhang and Yongxin Tong. Semantic Consistent Topic Discovery with Differential Privacy
  29. Yifei Zhang and Hao Zhu. Deep Neural Network for Collaborative Machine Learning with Additively Homomorphic Encryption
  30. Yiqiang Chen, Jindong Wang and Chaohui Yu. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
  31. Yu Liu, Chi Zhang, Yuehu Liu, Le Wang, Li Li and Nanning Zheng. Joint Intelligence Ranking by Federated Gradient Descent

Call for Papers

Privacy and security are becoming a key concern in our digital age. Companies and organizations are collecting a wealth of data on a daily basis. Data owners have to be very cautious while exploiting the values in the data, since the most useful data for machine learning often tend to be confidential. Increasingly strict data privacy regulations such as the European Union’s General Data Protection Regulation (GDPR) bring new legislative challenges to the big data and artificial intelligence (AI) community. Many operations in the big data domain, such as merging user data from various sources for building an AI model, will be considered illegal under the new regulatory framework if they are performed without explicit user authorization. More resources about federated learning can be found here.

In order to explore how the AI research community can adapt to this new regulatory reality, we organize this one-day workshop in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI'19). The workshop will focus on machine learning systems adhering to the privacy-preserving and security principles. Technical issues include but not limit to data collection, integration, training and modelling, both in the centralized and distributed setting. The workshop intends to provide a forum to discuss the open problems and share the most recent and ground-breaking work on the study and application of secure and privacy-preserving compliant machine learning. Both theoretical and application-based contributions are welcome. The FL series of workshops seek to explore new ideas with particular focus on addressing the following challenges:

  • Security and Regulation Compliance: How to meet the security and compliance requirements? Does the solution ensure data privacy and model security?
  • Collaboration and Expansion Solution: Does the solution connect different business partners from various parties and industries? Does the solution exploit and extend the value of data while observing user privacy and data security?
  • Promotion & Empowerment: Is the solution sustainable and intelligent? Does it include incentive mechanisms to encourage parties to participate on a continuous basis? Does it promote a stable and win-win business ecosystem?

We welcome submissions on recent advances in privacy-preserving, secure machine learning and artificial intelligence systems. All accepted papers will be presented during the workshop. At least one author of each accepted paper is expected to represent it at the workshop. Topics include but not limit to:


  1. Adversarial learning, data poisoning, adversarial examples, adversarial robustness, black box attacks
  2. Architecture and privacy-preserving learning protocols
  3. Federated learning and distributed privacy-preserving algorithms
  4. Human-in-the-loop for privacy-aware machine learning
  5. Incentive mechanism and game theory
  6. Privacy aware knowledge driven federated learning
  7. Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning
  8. Responsible, explainable and interpretability of AI
  9. Security for privacy
  10. Trade-off between privacy and efficiency


  1. Approaches to make AI GDPR-compliant
  2. Crowd intelligence
  3. Data value and economics of data federation
  4. Open-source frameworks for distributed learning
  5. Safety and security assessment of AI solutions
  6. Solutions to data security and small-data challenges in industries
  7. Standards of data privacy and security

Position, perspective, and vision papers are also welcome.

Submission Instructions

Submissions should be a maximum of 7 pages following the IJCAI-19 template with the 7th page containing nothing but references. We do not accept submissions of work currently under review. The submissions should include author details as we do not carry out blind review.

Submission link:

Journal Special Issue Publications

We will also invite high quality accepted papers to be extended for publication in a special issue in IEEE Intelligent Systems.

Organizing Committee

  • Steering Committee Chair:
    • Qiang Yang (Hong Kong University of Science and Technology / WeBank, China)
  • General Co-Chairs:
    • Yang Liu (WeBank, China)
    • Shiqiang Wang (IBM, USA)
    • Fausto Giunchiglia (University of Trento, Italy)
  • Publicity Co-Chairs:
    • Han Yu (Nanyang Technological University, Singapore)
    • Pingzhong Tang (Tsinghua University, China)
  • Local Arrangements Co-Chairs:
    • Xi Weng (Peking University, China)
    • Mingshu Cong (The University of Hong Kong, Hong Kong)
  • Publication Co-Chairs:
    • Yongxin Tong (Beihang University, China)
    • Junbo Zhang (, China)

Program Committee

  • Adria Gascon (The Alan Turing Institute / University of Warwick, UK)
  • Anis Elgabli (University of Oulu, Finland)
  • Aurélien Bellet (Inria, France)
  • Ayfer Ozgur (Stanford University, USA)
  • Boi Faltings (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
  • Chaoping Xing (Nanyang Technological University, Singapore)
  • Chaoyang He (University of Southern California, USA)
  • Dimitrios Papadopoulos (Hong Kong University of Science and Technology, Hong Kong)
  • Fabio Casati (University of Trento, Italy)
  • Farinaz Koushanfar (University of California San Diego, USA)
  • Gauri Joshi (Carnegie Mellon University, USA)
  • Graham Cormode (University of Warwick, UK)
  • Jalaj Upadhyay (Apple, USA)
  • Ji Feng (Sinovation Ventures AI Institute, China)
  • Jianshu Weng (AI Singapore, Singapore)
  • Jihong Park (University of Oulu, Finland)
  • Joshua Gardner (University of Michigan, USA)
  • Jun Zhao (Nanyang Technological University, Singapore)
  • Lalitha Sankar (Arizona State University, USA)
  • Leye Wang (Peking University, China)
  • Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
  • Mehdi Bennis (University of Oulu, Finland)
  • Nguyen Tran (The University of Sydney, Australia)
  • Praneeth Vepakomma (Massachusetts Institute of Technology, USA)
  • Prateek Mittal (Princeton University, USA)
  • Richard Nock (Data61, Australia)
  • Rui Lin (Chalmers University of Technology, Sweden)
  • Sewoong Oh (University of Illinois at Urbana-Champaign, USA)
  • Siwei Feng (Nanyang Technological University, Singapore)
  • Tara Javidi (University of California San Diego, USA)
  • Yihan Jiang (University of Washington, USA)
  • Yong Cheng (WeBank, China)
  • Zelei Liu (Nanyang Technological University, Singapore)

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