Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets

no code implementations • 8 Aug 2022 • Zhipeng Cheng, Xuwei Fan, Minghui LiWang, Ning Chen, Xianbin Wang

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets.

Federated Learning reinforcement-learning +1

Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges

no code implementations • 4 Jun 2022 • Zhipeng Cheng, Xuwei Fan, Minghui LiWang, Ning Chen, Xiaoyu Xia, Xianbin Wang

The ever-growing concerns regarding data privacy have led to a paradigm shift in machine learning (ML) architectures from centralized to distributed approaches, giving rise to federated learning (FL) and split learning (SL) as the two predominant privacy-preserving ML mechanisms.

BIG-bench Machine Learning Federated Learning +1

Energy-Aware Graph Task Scheduling in Software-Defined Air-Ground Integrated Vehicular Networks

no code implementations • 3 Aug 2020 • Minghui LiWang, Zhibin Gao, Xianbin Wang

Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation.

Scheduling