Scalable Hybrid Architecture for Wireless Collaborative Federated Learning (SHAFT)

与前几代移动网络不同，Beyond 5G(B5G)网络旨在支持边缘智能，即为终端用户提供通信和计算能力。无线边缘智能对于B5G的那些关键用例尤其重要，包括智能城市、自动驾驶、无线医疗、虚拟现实(VR)和增强现实(AR)游戏，在这些用例中，移动网络预计将配备智能功能，用于预测和塑造个人体验。联合学习(FL)是无线边缘智能的关键使能技术，它以分散的方式执行模型训练，并将数据保存在生成数据的地方。然而，由于具有异质资源和大量设备的复杂无线环境，从计算机网络到无线系统的直接自适应可能会在频谱和实现效率方面遭受性能下降。本项目的目的是通过有效利用移动通信环境的物理层动态特性并利用复杂的服务感知和资源感知协作边缘学习来开发一种新的可扩展的无线FL混合体系结构。该项目的新颖性是开发了这种新型的边缘学习体系结构，其中学习体系结构的基本限制由高级数学工具表征，如图论和随机学习。此外，通过应用压缩传感和机器学习等复杂工具，在存在实际限制的情况下量化具有挑战性的设计权衡的算法框架。

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.