Collaborative Research: CCRI: New: A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning

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Advances in machine learning have made a major impact on many real-world applications over the past decade, and have achieved scientific and engineering breakthroughs across many disciplines. A new era of collaborative learning is emerging as part of the next phase of ubiquitous computing, wherein researchers at different sites will work together to correlate the disparate data they have separately acquired and eventually create a sophisticated decision-making model. It is thus imperative to establish a platform to support collaborative, multi-party data analysis, through which the participating parties can share their data with each other with different degrees of privacy control. The participants can compute with each other's data, by either directly sharing data with the server or only sharing their model parameters with the server to collaboratively derive a solution with other parties. To make such an environment available to the community, this project establishes a scalable and trusted hardware and software environment, termed Bridge, to support a general form of collaborative machine learning. The Bridge platform enables scalable multi-party learning and data analysis in a variety of forms, in both centralized and decentralized settings, with security and privacy guarantees. The project's novelties are to synergistically design and integrate both hardware and software innovation as well as a suite of security and privacy mechanisms and tools to support various types of multi-party machine learning. The project's impacts are to enable collaborative research efforts in diverse communities of CISE researchers pursuing focused research agendas in computer and information science and engineering, and generate enormous social and economic benefits to individuals and organizations. The minority students and under-served populations will be engaged in research activities to create an inclusive environment where everyone contributes to and benefits from cutting-edge scientific research.The Bridge platform will develop a unified hardware and software infrastructure to achieve hardware and software co-design for multi-party learning. An algorithmic software infrastructure is designed to support distributed, federated, and multi-modal model learning and sharing. The Bridge platform integrates cryptographic (secure multi-party computation) and noise-based methods (differential privacy) to provide privacy across the entire process from data collection to output. The Bridge platform provides a set of tools on integrated data access, AutoML, team creation, machine learning model vulnerability evaluation, and heterogeneous feature embeddings to support flexible user applications. The Bridge platform ensures the scalability in the number of tasks, the number of users, and heterogeneity of data types by developing advanced techniques to improve asynchronous model updates, communication efficiency, fast convergence, and vertical data partition. The Bridge platform builds a collaborative learning community and accelerates many new research areas in the core Computer and Information Science and Engineering (CISE), such as advanced machine learning and data science, data privacy and trustworthy AI, convergent research among hardware, software and machine learning, and intelligent internet of things.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.