Scalable Hybrid Architecture for Wireless Collaborative Federated Learning (SHAFT)

用于无线协作联合学习 (SHAFT) 的可扩展混合架构

• 批准号: EP/W034786/1
• 负责人: Arumugam Nallanathan
• 金额: $ 56.76万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW034786%2F1
• 关键词: Scalable; Hybrid; Architecture; Wireless; Collaborative

Unlike previous generations of mobile networks, the beyond 5G (B5G) network is envisioned to support edge intelligence, which is to provide both communication and computing capabilities to the proximity of end users. Wireless edge intelligence is particularly important to those crucial use cases of B5G, including smart cities, autonomous driving, wireless healthcare, virtual reality (VR) and augmented reality (AR) gaming, where mobile networks are expected to be equipped with intelligent capabilities for prediction and shaping experiences to individuals. Federated learning (FL) is a key enabling technology for wireless edge intelligence, by performing the model training in a decentralized manner and keeping the data where it is generated. However, a straightforward adaption of FL from computer networks to wireless systems can suffer performance degradation in spectral and implementation efficiency, because of the complex wireless environment with heterogeneous resources and a massive number of devices. The aim of this project is to develop a novel scalable hybrid architecture for wireless FL by efficiently utilising the physical layer dynamics of the mobile communication environments and exploiting sophisticated service-aware and resource-aware collaborative edge learning. The novelty of the project is the development of this novel edge learning architecture, where the fundamental limits of the learning architecture is characterised by advanced mathematical tools, such as graph theory and stochastic learning. In addition, an algorithmic framework for quantifying challenging design trade-offs in the presence of practical constraints by applying sophisticated tools such as compressed sensing and machine learning.

与前几代移动的网络不同，超5G（B5G）网络被设想为支持边缘智能，即向终端用户附近提供通信和计算能力。无线边缘智能对于B5G的关键用例尤其重要，包括智能城市、自动驾驶、无线医疗保健、虚拟现实（VR）和增强现实（AR）游戏，其中移动的网络预计将配备智能功能，用于预测和塑造个人体验。联邦学习（FL）是无线边缘智能的关键技术，它以分散的方式执行模型训练，并将数据保存在生成的地方。然而，FL从计算机网络到无线系统的直接适配可能会在频谱和实现效率方面遭受性能降级，因为具有异构资源和大量设备的复杂无线环境。该项目的目的是开发一种新的可扩展的混合架构的无线FL有效地利用物理层动态的移动的通信环境，并利用先进的服务感知和资源感知的协作边缘学习。该项目的新奇在于开发了这种新型的边缘学习架构，其中学习架构的基本限制是由高级数学工具（如图论和随机学习）表征的。此外，一个算法框架，通过应用复杂的工具，如压缩传感和机器学习，量化具有挑战性的设计权衡在实际约束的存在。

项目成果

Exploring Representativity in Device Scheduling for Wireless Federated Learning

• DOI: 10.1109/twc.2023.3281765
• 发表时间: 2024-01
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zhixiong Chen;Wenqiang Yi;A. Nallanathan

Knowledge-Aided Federated Learning for Energy-Limited Wireless Networks

• DOI: 10.1109/tcomm.2023.3261383
• 发表时间: 2022-09
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Zhixiong Chen;Wenqiang Yi;Yuanwei Liu;A. Nallanathan

Adaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learning

• DOI: 10.1109/twc.2023.3342626
• 发表时间: 2023
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zhixiong Chen;Wenqiang Yi;Hyundong Shin;Arumgam Nallanathan

Robust Federated Learning for Unreliable and Resource-limited Wireless Networks

• DOI: 10.1109/twc.2024.3366393
• 发表时间: 2024
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zhixiong Chen;Wenqiang Yi;Yuanwei Liu;Arumgam Nallanathan

Distributed Digital Twin Migration in Multi-Tier Computing Systems

• DOI: 10.1109/jstsp.2024.3359009
• 发表时间: 2024-01
• 期刊: IEEE Journal of Selected Topics in Signal Processing
• 影响因子: 7.5
• 作者: Zhixiong Chen;Wenqiang Yi;Arumgam Nallanathan;Jonathon A. Chambers