Latency-Efficient Wireless Federated Learning With Quantization and Scheduling

Yan, Zhigang; Li, Dong; Yu, Xianhua; Zhang, Zhichao

doi:10.1109/lcomm.2022.3199490

Cited by 10 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, FL faces notable hurdles in terms of communication efficiency as the number of participating devices and model parameters continues to grow [9], [10]. To address this issue, the majority of research efforts have been directed towards parameter compression [11]- [13], device selection [13]- [16], and power/bandwidth allocation algorithms [17]- [19] within the FL framework. Despite these endeavors, the conventional FL paradigms continue to rely on a central node for parameter aggregation, leading to heightened communication overhead and vulnerability associated with a single point of failure [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Federated Cooperation and Augmentation for Power Allocation in Decentralized Wireless Networks

et al. 2020

View full text Add to dashboard Cite

Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of power and computing resource consumptions. In the meantime, the volume of data generated at the edge networks is also increasing rapidly. Under this circumstance, building energy-efficient and privacy-protected communications is imperative for 5G and beyond wireless communication systems. The recent emerging distributed learning methods such as federated learning (FL) perform well in improving resource efficiency while protecting user privacy with low communication overhead. Specifically, FL enables edge devices to learn a shared network model by aggregating local updates while keeping all the training processes on local devices. This paper investigates distributed power allocation for edge users in decentralized wireless networks with aim to maximize energy/spectrum efficiency while preventing privacy leakage based on a FL framework. Due to the dynamics and complexity of wireless networks, we adopt an on-line Actor-Critic (AC) architecture as the local training model, and FL performs cooperation for edge users by sharing the gradients and weightages generated in the Actor network. Moreover, in order to resolve the over-fitting problem caused by data leakages in Non-independent and identically distributed (Non-i.i.d) data environment, we propose a federated augmentation mechanism with Wasserstein Generative Adversarial Networks (WGANs) algorithm for data augmentation. Federated augmentation empowers each device to replenish the data buffer using a generative model of WGANs until accomplishing an i.i.d training dataset, which significantly reduces the communication overhead in distributed learning compared to direct data sample exchange method. Numerical results reveal that the proposed federated learning based cooperation and augmentation (FL-CA) algorithm possesses a good convergence property, high robustness and achieves better accuracy of power allocation strategy than other three benchmark algorithms. INDEX TERMS Federated learning, power allocation, wireless networks, federated cooperation, federated augmentation.

show abstract