The 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19)

in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19)

 

Venue:      Macau

Date:         Aug 12, 2019

Submission Website:    https://easychair.org/conferences/?conf=fml2019

Download Workshop Presentations here: FL-IJCAI’19 ppts


Important Dates

  • May 26, 2019 : Deadline for paper submission (11:59PM UTC-12)
  • June 24, 2019: Notification of acceptance
  • July 15, 2019July 22, 2019: Final version due
  • Aug 12, 2019: FML’19 Workshop

Introduction

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 unlocking the values in the data, since the most useful data for machine learning often tend to be confidential. The European Union’s General Data Protection Regulation (GDPR) brings 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 28th International Joint Conference on Artificial Intelligence (IJCAI-19). The workshop will focus on machine learning systems with privacy and security. 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 GDPR compliant machine learning. The workshop will also serve as a venue for networking. Researchers from different communities interested in this problem will have ample time to share thoughts and experience, promoting possible long-term collaborations. Both theoretical and application-based contributions are welcome.

Scope

The FML 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 and 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. Selected high quality contributions will be invited for submission in a journal special issue for publication. 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

Awards and Journal Special Issue Publications

One Best Paper Award and one Best Presentation Award will be given out during the workshop. We will also invite high quality accepted papers to be extended for publication in a special issue in the IEEE Intelligent Systems journal (Impact Factor: 2.596).

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 recently published or currently under review. The submissions should include author details as we do not carry out blind review.

Submission link: https://easychair.org/conferences/?conf=fml2019

For any questions, please send an email to: FML_contact@fedai.org

 

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