MLWiNS: Distributed Learning for the Nomadic Edge

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Edge computing is a paradigm that brings computing closer to where the data is generated, such as cellular networks, internet service providers, and enterprise networks. Typical edge nodes are static and are located in fixed points over a wired network. This project creates a new notion of edge computing, Nautilus, to support distributed machine learning over mobile nodes, such as vehicles. The project aims to create new techniques that can enable a new class of applications that leverage sensors mounted on vehicles. The applications range from smart transportation, to urban planning, and to broadly support smarter cities and communities. The broader impact of this project includes: (i) creation of a hands-on curriculum centered around computer vision techniques and machine learning; (ii) educating the broader public on wireless systems and machine learning technologies and their impact on our society; (iii) engagement with industrial partners and with regulatory bodies; (iv) public dissemination of the developed source code and prototype design; (v) mentoring of women STEM students through various campus programs as well as global ones and helping them identify opportunities for fulfilling careers in this domain.This project is structured in a three-tiered architecture consisting of the nomadic edge (in vehicles), the static edge (co-located with Radio Access Networks), and the cloud. To support diverse emerging applications across domains (e.g., smart transportation, urban planning, and more broadly across different aspects of smarter communities) which can issue queries spanning large spatio-temporal regions. The research agenda includes five complementary tasks: (i) Design of distributed and dynamic computer vision from the vehicular context, which involves collaborative and federated machine learning approaches to event and object detection under diverse conditions; (ii) Design of collaborative and distributed training and inference to address high level queries which will utilize various techniques of redundant and coded computing and communication to support efficiency and scalability; (iii) Estimation and prediction of the wireless network context to infer opportunities for communication between collaborating nomadic edge nodes, as well as between the nomadic and static edges; (iv) Design for privacy and security of both data and models; and (v) Systems integration and field trials to allow for technique refinement, evaluation, and reproducibility. Through a partnership with the local transit network — Madison Metro Transit, the research team plans to use the project research results and assist them in understanding various challenges, opportunities, and optimizations in planning various transportation and transit decisions.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.