Distributed Learning Systems
We vision that distributed learning systems (DLS) will be ubiquitously deployed in future edge computing systems. DLS supports large-scale data-driven decision-making, monitoring, and recommendation systems. Our application scenarios include connected autonomous vehicles (CAVs), augmented reality (AR), virtual reality (VR), and industrial Internet of things (IIoTs). The distributed systems are based on federated learning technology aiming at reducing communication costs and preserving privacy.

Edge Computing
The deployments of edge computing systems have been growing rapidly. Our research areas in edge computing include resource allocation, task scheduling, placement decision making, and availability assurance.

Container orchestration systems and AIOps
With our strong industrial backgrounds, we pay close attention to production-grade cloud and edge computing orchestration. We combine our research with Kubernetes-compatible systems running on both cloud and edge environments. Our research includes edge-based resource allocation and placement, privacy protection, service scaling and recovery, and edge application deployment with high availability.

Blockchain Services
We are experienced in blockchain research and cryptocurrency asset management. We leverage blockchain technology for the decentralization of data aggregation services in federated learning.