Data-driven Distributionally Robust Optimization for Edge Intelligence

• Published in: Annual Joint Conference of the IEEE Computer and Communications Societies pp. 2619 - 2628
• Main Authors: Zhang, Zhaofeng, Lin, Sen, Dedeoglu, Mehmet, Ding, Kemi, Zhang, Junshan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• Subjects: collaborative learning; Computational modeling; Data models; Decision making; distributionally robust optimization; Edge intelligence; Knowledge transfer; Kullback-Leibler divergence; Real-time systems; Robustness; Uncertainty; Wasserstein distance

The past few years have witnessed the explosive growth of Internet of Things (IoT) devices. The necessity of real-time edge intelligence for IoT applications demands that decision making must take place right here right now at the network edge, thus dictating that a high percentage of the IoT create...