AI Singapore Research Grant 2020 Award
Toward Trustable Model-centric Sharing for Collaborative Machine Learning
Collaborative machine learning is an appealing paradigm to build high-quality machine learning (ML) models by training on the data from many parties. However, concerns over trust and security have hindered the sharing of data between organizations. Much efforts have been focused on breaking the “data silos”, but the silos are inherently difficult to break at the data level, as personal or proprietary data are often involved.
We posit that the ML models can be more amenable for sharing as they are inherently more compact and self-contained with purpose-compiled knowledge from the data. Rather than requiring the learning collaborators to contribute their private data, this project will focus on enabling collaborative machine learning through allowing the collaborators to share heterogeneous black-box models, and to be appropriately incentivized based on their self-interests. Given that most current research are focused on the data level, this project will develop new model-centric collaborative machine learning methods, as well as new notions for trustable model-centric sharing and effective model management techniques for real-world model-centric platforms.
This 4-year project will be led by Prof See-Kiong Ng and be conducted by the leading international and inter-disciplinary AI and data science researchers in the institute’s new Trusted Collaborative Machine Learning (Trusted CollabML) Lab. The CollabML Lab is a collaboration between the Institute of Data Science, the Department of Computer Science, and the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), the School of Law at the Singapore Management University (SMU), as well as the Departments of Electrical Engineering and Computer Science at University of California, Berkeley (UC Berkeley) and Massachusetts Institute of Technology (MIT). The project team will design and develop a trustable, operational and legally-compliant model-centric sharing platform, validated with real-world data and applications provided by industry partners. Such a platform will be instrumental for powering a data-driven AI innovation ecosystem for a vibrant digital economy through trusted self-interest-driven sharing of learned machine intelligence.
Principal and Co-Principal Investigators
Principal Investigator: Professor See-Kiong Ng (IDS)
Co-Principal Investigator (Local): Associate Professor He Bingsheng (NUS), Professor Wynne Hsu (NUS), Professor Lee Mong Li (NUS), Associate Professor Vincent Tan Yan Fu (NUS), Associate Professor Warren B. Chik (SMU)
Co-Principal Investigator (International): Professor Dawn Song (UC Berkeley), Professor Patrick Jaillet (MIT)
Collaborators:
Associate Professor Bryan Kian Hsiang Low (NUS), Associate Professor Sinno Jialin Pan (NTU), Dr. Nghia Hoang (Amazon)
Current Project Publications:
Click here for a list of publications from the project team, most of which are in the top 10% of S&T journals and conferences in the field as tracked in a list approved by NRF and/or in the Clarivate Analytics Journal Citation Reports.
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This collaboration is funded by National Research Foundation, Singapore under its AI Singapore Programme.