CAREER: Foundations of Collaborative Machine Learning

Recent advances in machine learning rely on collecting an enormous amount of data and learning immense models in a centralized cloud. However, the excessive storage and computational needs of centralized approaches, alongside regulatory challenges in sharing private data, put the utility of this paradigm in doubt. Collaborative machine learning is a recent alternative paradigm to tackle these issues by developing algorithms collaboratively without exchanging or centralizing the data. For example, different geographically distributed hospitals, each being in possession of limited patients’ data, may collaboratively develop predictive algorithms to improve diagnostics and treatment beyond what could be accomplished alone. Unlocking the full potential of collaborative learning strongly depends on the ability to encourage a large pool of individuals or corporations to share their private data and resources. Towards this aim, this CAREER award offers an intersectional approach to develop theoretically-grounded collaborative algorithms to facilitate learning optimally from fragmented, heterogeneous private data under resource constraints by jointly addressing various computational, statistical, systems, and game-theoretic challenges. By promoting a stable and fair ecosystem to benefit and retain all participants, without imposing stringent data and resource constraints, this project’s outcomes promise to make data-driven intelligent systems more effective, personalized, and robust in a myriad of application domains, such as personalized healthcare, precision agriculture, and education.To promote a healthy data and compute ecosystem and enable optimal use of distributed heterogeneous data under resource constraints, this project offers an intersectional approach to rigorously address computational, statistical, and game-theoretic challenges. On the practical side, it introduces a pluralistic learning paradigm and develops distributed algorithms that are cognizant of statistical heterogeneity and confined to learning models that meet available resources. On the statistical side, the project focuses on establishing generalization guarantees and understanding information-theoretic tradeoffs, providing an opportunity for synergistic advancements and insights. The project makes an intimate connection between collaborative learning and aggregative games and leverages developed theoretical and algorithmic investigations to answer questions related to equilibrium, incentivization, fairness, and stability to promote a healthy ecosystem. The intersectional and unified study the project proposes, creates essential connections, and fosters new transformative methods not developed by efforts within the individual disciplines. The research will be integrated with education through hosting workshops, mentoring students, and developing courses.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.