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FL-IJCAI'20 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020

Pacifico Yokohama, Yokohama, Japan

Event Date:	July 13, 2020 - July 13, 2020
Submission Deadline:	April 26, 2020
Notification of Acceptance:	May 24, 2020

About

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 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?

Call for Papers

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
Federated learning and distributed privacy-preserving algorithms
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

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.

Summary

FL-IJCAI'20 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 will take place in Pacifico Yokohama, Yokohama, Japan. It’s a 1 day event starting on Jul 13, 2020 (Monday) and will be winded up on Jul 13, 2020 (Monday).

FL-IJCAI'20 falls under the following areas: ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, etc. Submissions for this Workshop can be made by Apr 26, 2020. Authors can expect the result of submission by May 24, 2020.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the FL-IJCAI'20

Short Name: FL-IJCAI'20
Full Name: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020
Timing: 09:00 AM-06:00 PM (expected)
Fees: Check the official website of FL-IJCAI'20
Event Type: Workshop
Website Link: https://www.ntu.edu.sg/home/han.yu/fl-ijcai20.html
Location/Address: Pacifico Yokohama, Yokohama, Japan

Credits and Sources

[1] FL-IJCAI'20 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020