no code implementations • 29 Jun 2021 • Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova, Christopher G. Brinton, Mung Chiang
The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions.
no code implementations • 17 May 2023 • Lin Duan, Jingwei Sun, Yiran Chen, Maria Gorlatova
Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preserving privacy.
1 code implementation • 11 Jan 2022 • Ying Chen, Hojung Kwon, Hazer Inaltekin, Maria Gorlatova
The importance of the dynamics of the viewport pose, i. e., the location and the orientation of users' points of view, for virtual reality (VR) experiences calls for the development of VR viewport pose models.
1 code implementation • 11 Jan 2023 • Ying Chen, Hazer Inaltekin, Maria Gorlatova
Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge.