International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI'20)
FL-IJCAI 2020
- URL: https://www.ntu.edu.sg/home/han.yu/fl-ijcai20.html
- Event Date: 2020-07-13 ~ 2020-07-13
- Submission Date: 2020-04-26
- Location: Yokohama, Japan
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 at (https://www.ntu.edu.sg/home/han.yu/FL.html).
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 29th International Joint Conference on Artificial Intelligence (IJCAI-20). 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:
Techniques
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
Applications
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.
Special Benchmarking Track
In addition, the workshop will also encourage researchers to demonstrate and test their ideas based on a set of benchmark datasets (https://dataset.fedai.org/#/). To this end, the special benchmarking track calls for submissions that evaluate the proposed methods using the benchmark datasets. If your submission uses the aforementioned datasets for experimental evaluation, please select option (B) or (C) from the "Submission Details" dropdown list.
Submission Instructions
Submissions should be between 4 to 7 pages following the IJCAI-20 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.
Submission link: https://easychair.org/conferences/?conf=flijcai20
Awards
One Best Paper Award and one Best Student Paper Award will be given out during the workshop. One Special Track Distinguished Paper Award winner will be selected from the Special Benchmarking Track submissions.
Organizing Committee
Steering Committee Chair:
Qiang Yang (WeBank, China/Hong Kong University of Science and Technology, Hong Kong)
General Co-Chairs:
Lixin Fan (WeBank, China)
Martin Pelikan (Apple, USA)
Program Co-Chairs:
Han Yu (Nanyang Technological University, Singapore)
Yiran Chen (Duke University, USA)
Local Arrangements Co-Chairs:
Kilho Shin (Gakushuin University, Japan)
Takayuki Ito (Nagoya Institute of Technology, Japan)
Tianyu Zhang (WeBank, China)
Special Track Co-Chairs:
Bingsheng He (National University of Singapore, Singapore)
Di Jiang (WeBank, China)
Yang Liu (WeBank, China)
Publicity Co-Chairs:
Boyang Li (Nanyang Technological University, Singapore)
Lingjuan Lyu (National University of Singapore, Singapore)
Program Committee (Tentative)
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)
Bingsheng He (National University of Singapore, Singapore)
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 (Sinnovation 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)
Keith Bonawitz (Google, USA)
Lalitha Sankar (Arizona State University, USA)
Leye Wang (Peking University, China)
Marco Gruteser (Google, USA)
Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
Mehdi Bennis (University of Oulu, Finland)
Mingshu Cong (The University of Hong Kong, Hong Kong)
Nguyen Tran (The University of Sydney, Australia)
Peter Kairouz (Google, USA)
Pingzhong Tang (Tsinghua University, China)
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)
Shiqiang Wang (IBM, USA)
Siwei Feng (Nanyang Technological University, Singapore)
Tara Javidi (University of California San Diego, USA)
Xi Weng (Peking University, China)
Yihan Jiang (University of Washington, USA)
Yong Cheng (WeBank, China)
Yongxin Tong (Beihang University, China)
Zelei Liu (Nanyang Technological University, Singapore)
Zheng Xu (University of Science and Technology of China, China)
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 at (https://www.ntu.edu.sg/home/han.yu/FL.html).
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 29th International Joint Conference on Artificial Intelligence (IJCAI-20). 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:
Techniques
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
Applications
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.
Special Benchmarking Track
In addition, the workshop will also encourage researchers to demonstrate and test their ideas based on a set of benchmark datasets (https://dataset.fedai.org/#/). To this end, the special benchmarking track calls for submissions that evaluate the proposed methods using the benchmark datasets. If your submission uses the aforementioned datasets for experimental evaluation, please select option (B) or (C) from the "Submission Details" dropdown list.
Submission Instructions
Submissions should be between 4 to 7 pages following the IJCAI-20 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.
Submission link: https://easychair.org/conferences/?conf=flijcai20
Awards
One Best Paper Award and one Best Student Paper Award will be given out during the workshop. One Special Track Distinguished Paper Award winner will be selected from the Special Benchmarking Track submissions.
Organizing Committee
Steering Committee Chair:
Qiang Yang (WeBank, China/Hong Kong University of Science and Technology, Hong Kong)
General Co-Chairs:
Lixin Fan (WeBank, China)
Martin Pelikan (Apple, USA)
Program Co-Chairs:
Han Yu (Nanyang Technological University, Singapore)
Yiran Chen (Duke University, USA)
Local Arrangements Co-Chairs:
Kilho Shin (Gakushuin University, Japan)
Takayuki Ito (Nagoya Institute of Technology, Japan)
Tianyu Zhang (WeBank, China)
Special Track Co-Chairs:
Bingsheng He (National University of Singapore, Singapore)
Di Jiang (WeBank, China)
Yang Liu (WeBank, China)
Publicity Co-Chairs:
Boyang Li (Nanyang Technological University, Singapore)
Lingjuan Lyu (National University of Singapore, Singapore)
Program Committee (Tentative)
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)
Bingsheng He (National University of Singapore, Singapore)
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 (Sinnovation 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)
Keith Bonawitz (Google, USA)
Lalitha Sankar (Arizona State University, USA)
Leye Wang (Peking University, China)
Marco Gruteser (Google, USA)
Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
Mehdi Bennis (University of Oulu, Finland)
Mingshu Cong (The University of Hong Kong, Hong Kong)
Nguyen Tran (The University of Sydney, Australia)
Peter Kairouz (Google, USA)
Pingzhong Tang (Tsinghua University, China)
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)
Shiqiang Wang (IBM, USA)
Siwei Feng (Nanyang Technological University, Singapore)
Tara Javidi (University of California San Diego, USA)
Xi Weng (Peking University, China)
Yihan Jiang (University of Washington, USA)
Yong Cheng (WeBank, China)
Yongxin Tong (Beihang University, China)
Zelei Liu (Nanyang Technological University, Singapore)
Zheng Xu (University of Science and Technology of China, China)
Journal Promotion
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| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
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| Wind and Structures, An International Journal (Wind and Structures) |
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