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.
Featured
A flexible FL platform with GUI.