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FTL-IJCAI 2021 : International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021

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Link: http://federated-learning.org/fl-ijcai-2021/
 
When Aug 21, 2021 - Aug 22, 2021
Where Online
Submission Deadline Jun 20, 2021
Notification Due Jul 15, 2021
Final Version Due Aug 1, 2021
Categories    artificial intelligence   machine learning   federated learning   transfer learning
 

Call For Papers

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.

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 30th International Joint Conference on Artificial Intelligence (IJCAI'21). 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 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:

Techniques
- Adversarial learning, data poisoning, adversarial examples, adversarial robustness, black box attacks
- Architecture and privacy-preserving learning protocols
- Automated 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
- Privacy aware knowledge driven federated learning
- Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning
- Responsible, explainable and interpretability of AI
- Security for privacy
- Trade-off between privacy and efficiency
- Heterogeneous computing systems for federated learning

Applications
- Approaches to make AI GDPR-compliant
- Crowd intelligence
- Data value and economics of data federation
- Open-source frameworks for distributed learning
- Safety and security assessment of AI solutions
- Solutions to data security and small-data challenges in industries
- Standards of data privacy and security
- Position, perspective, and vision papers are also welcome.

Position, perspective, and vision papers are also welcome.

More information on previous workshops can be found at http://www.federated-learning.org/.


Submission Instructions
Submissions should be between 4 to 7 pages following the IJCAI-21 template. Formatting guidelines, including LaTeX styles and a Word template, can be found at: https://www.ijcai.org/authors_kit. We do not accept submissions of work currently under review. The submissions should include author details as we do not carry out blind review. High quality submissions will be invited to submit an extended version to a journal special issue (to be announced later).

Submission link: https://easychair.org/conferences/?conf=flijcai21

For enquiries, please email to flijcai21@easychair.org

Organizing Committee
Steering Chair:
- Qiang Yang (Hong Kong University of Science and Technology / WeBank, China)
General Co-Chairs:
- Lixin Fan (WeBank, China)
- Heng Huang (JD Finance America Corporation / University of Pittsburgh, USA)
- Sinno Jialin Pan (Nanyang Technological University, Singapore)
Program Co-Chairs:
- Han Yu (Nanyang Technological University, Singapore)
- Wei-Wei Tu (4Paradigm Inc., China)
- Yang Liu (WeBank, China)
Publicity Co-Chairs:
- Yongxin Tong (Beihang University, China)
- Guodong Long (University of Technology Sydney, Australia)
- Boyang Li (Nanyang Technological University, Singapore)
Publication Co-Chairs:
- Yuan Liu (Northeastern University, China)
- Ya-Ching Lu (Clustar, China)
- Xu Guo (Nanyang Technological University, Singapore)
Web Co-Chairs:
- Jun Lin (Joint SDU-NTU Centre for AI Research (C-FAIR), China)
- Chang Liu (Nanyang Technological University, Singapore)

Program Committee
- Yiqiang Chen (Institute of Computing Technology, Chinese Academy of Sciences, China)
- Zichen Chen (Joint NTU-WeBank Research Centre on Fintech, Singapore)
- Bingsheng He (National University of Singapore, Singapore)
- Chaoyang He (University of Southern California, USA)
- Shuihai Hu (Clustar, China)
- Yihan Jiang (University of Washington, USA)
- Pallika Kanani (Oracle Labs, USA)
- Bryan Wei Yang Lim (Alibaba-NTU Singapore Joint Research Institute, Singapore)
- Zelei Liu (Nanyang Technological University, Singapore)
- Songtao Lu (IBM Thomas J. Watson Research Center, USA)
- Virendra Marathe (Oracle Labs, USA)
- Dimitrios Papadopoulos (The Hong Kong University of Science and Technology, Hong Kong)
- Yiyang Pei (Singapore Institute of Technology, Singapore)
- Anit Kumar Sahu (Bosch Center for Artificial Intelligence, Germany)
- Yueyue Shi (South China University of Technology, China)
- Lifeng Sun (Tsinghua University, China)
- Alysa Ziying Tan (Alibaba-NTU Singapore Joint Research Institute, Singapore)
- Aleksei Triastcyn (Google, USA)
- Praneeth Vepakomma (Massachusetts Institute of Technology, USA)
- Shiqiang Wang (IBM Thomas J. Watson Research Center, USA)
- Xi Weng (Peking University, China)
- Jianyu Wang (Carnegie Mellon University, USA)
- Jianshu Weng (AI Singapore, Singapore)
- Pengwei Xing (Nanyang Technological University, Singapore)
- Zheng Xu (University of Maryland, USA)
- Xin Yao (Tsinghua University, China)
- Junxue Zhang (Clustar, China)
- Peng Zhang (Guangzhou University, China)
- Rui-Xiao Zhang (Tsinghua University, China)
- Tianwei Zhang (Nanyang Technological University, Singapore)

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