International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality
in Conjunction with IJCAI 2021 (FTL-IJCAI'21)


Submission Due: May 05, 2021 (23:59:59 AoE)
Notification Due: May 25, 2021
Workshop Date: August 21~22, 2021
Venue: Online

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:

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. Automated federated learning
  4. Federated learning and distributed privacy-preserving algorithms
  5. Federated transfer learning
  6. Human-in-the-loop for privacy-aware machine learning
  7. Incentive mechanism and game theory
  8. Privacy aware knowledge driven federated learning
  9. Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning
  10. Responsible, explainable and interpretability of AI
  11. Security for privacy
  12. Trade-off between privacy and efficiency
  13. Heterogeneous computing systems for federated learning

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.

More information on previous workshops can be found here.


Distinguished Keynote Lecture

   

Title: A Journey from Transfer Learning to Federated Learning

Speaker: Qiang Yang, Chief AI Officer (CAIO), WeBank / Chair Professor, Hong Kong University of Science and Technology (HKUST)

Biography
Qiang Yang is the head of the AI Department at WeBank (Chief AI Officer) and Chair Professor at the Computer Science and Engineering (CSE) Department of the Hong Kong University of Science and Technology (HKUST), where he was a former head of CSE Department and founding director of the Big Data Institute (2015-2018). His research interests include AI, machine learning, and data mining, especially in transfer learning, automated planning, federated learning, and case-based reasoning. He is a fellow of several international societies, including ACM, AAAI, IEEE, IAPR, and AAAS. He received his Ph.D. in Computer Science in 1989 and his M.Sc. in Astrophysics in 1985, both from the University of Maryland, College Park. He obtained his B.Sc. in Astrophysics from Peking University in 1982. He had been a faculty member at the University of Waterloo (1989-1995) and Simon Fraser University (1995-2001). He was the founding Editor-in-Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and IEEE Transactions on Big Data (IEEE TBD). He served as the President of International Joint Conference on AI (IJCAI, 2017-2019) and an executive council member of Association for the Advancement of AI (AAAI, 2016-2020). Qiang Yang is a recipient of several awards, including the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award (2017), and AAAI Innovative Applications of AI Award (2018 and 2020). He was the founding director of Huawei's Noah's Ark Lab (2012-2014) and a co-founder of 4Paradigm Corp, an AI platform company. He is an author of several books including Intelligent Planning (Springer), Crafting Your Research Future (Morgan & Claypool), and Constraint-based Design Recovery for Software Engineering (Springer).


Invited Talks

   

Title: Towards Robust and Efficient Federated Learning

Speaker: Shiqiang Wang, IBM T. J. Watson Research Center, USA

Biography
Shiqiang Wang received his Ph.D. from the Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom, in 2015. He is a Research Staff Member at IBM T. J. Watson Research Center, NY, USA since 2016, where he was also a Graduate-level Co-op in the summers of 2014 and 2013. In the fall of 2012, he was at NEC Laboratories Europe, Heidelberg, Germany. His current research focuses on the interdisciplinary areas in machine learning, distributed systems, optimization, networking, and signal processing. Dr. Wang served as a technical program committee (TPC) member of several international conferences, including ICML, ICDCS, AISTATS, IJCAI, WWW, IFIP Networking, IEEE GLOBECOM, IEEE ICC, and as an associate editor of the IEEE Transactions on Mobile Computing (starting in 2021). He received the IBM Outstanding Technical Achievement Award (OTAA) in 2019, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015.


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


Program Committee


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