Over-the-Air Federated Averaging with Limited Power and Privacy Budgets

no code implementations • 5 May 2023 • Na Yan, Kezhi Wang, Cunhua Pan, Kok Keong Chai, Feng Shu, Jiangzhou Wang

We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints.

Federated Learning Scheduling

Device Scheduling for Over-the-Air Federated Learning with Differential Privacy

no code implementations • 31 Oct 2022 • Na Yan, Kezhi Wang, Cunhua Pan, Kok Keong Chai

The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system.

Federated Learning Scheduling

Toward Secure and Private Over-the-Air Federated Learning

no code implementations • 14 Oct 2022 • Na Yan, Kezhi Wang, Kangda Zhi, Cunhua Pan, Kok Keong Chai, H. Vincent Poor

In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL) framework is studied where noise is employed to protect data privacy and system security.

Federated Learning Scheduling +1

MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks

no code implementations • 16 Feb 2022 • Zhichen Ni, Honglong Chen, Zhe Li, Xiaomeng Wang, Na Yan, Weifeng Liu, Feng Xia

The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge.

Edge-computing