Mobile online learning is challenged by the inherent conflict between dynamically evolving pedagogical demands and rigid resource provisioning infrastructures. Conventional quality of service (QoS)-driven resource allocation paradigms suffer from two critical limitations: 1) limited responsiveness to temporal-spatial f...
Ming-Zi Chen, Pei-Shun Yan, Hong-Jun Li et al.· IEEE Transactions on Mobile...· 1 citation· ⚡1
The flexible architecture of the open radio access network (O-RAN) provides effective support for the deployment of federated learning (FL). However, existing FL schemes in wireless networks often suffer from packet errors, which lead to reduced model test accuracy and increased training delay. In this paper, we propos...
Kai Qiao, Hongchao Wang, Zi-Hao Zhang et al.· IEEE Transactions on Mobile...· 0 citations
In large areas, mobile edge computing (MEC) systems enabled by drones, also known as unmanned aerial vehicles (UAVs), can provide flexible edge computing services and facilitate low-altitude inspection. Such systems are primarily limited by the computing resources and energy of the drone, as well as their reliance on c...
W. Qi, Wei-Feng Zhong, Jia-Wen Kang et al.· IEEE Transactions on Mobile...· 1 citation
Dispersed computing has emerged as a promising paradigm that leverages underutilized resources from massive Internet of Things devices (IoTDs) to enhance the computing capacity at the network edge. However, existing works about the dispersed computing overlook the heterogeneous computing environment with parallel and s...
Xumin Huang, Zexiong Wu, Chaoda Peng et al.· IEEE Transactions on Mobile...· 2 citations
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