Real-Time Federated Learning at the Wireless Edge via Algorithm-Hardware Co-Design

通过算法-硬件协同设计在无线边缘进行实时联合学习

基本信息

批准号：
EP/X019160/1
负责人：
JIA HU
金额：
$ 25.67万
依托单位：
UNIVERSITY OF EXETER
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%2FX019160%2F1
关键词：
Real Time Federated Learning Wireless

项目摘要

The past years have witnessed a rapidly growing number of wirelessly-connected devices such as smartphones and Internet-of-Things (IoT) equipment, which generate ever-increasing amounts of data driving key Artificial Intelligence (AI) applications. However, users are increasingly unwilling to allow their private data (such as media, location, or sensor data) to be uploaded to a central location (e.g., cloud datacentre) for training Machine Learning (ML) models, and data-protection laws such as the Data Protection Act 2018 are growing more restrictive towards data usage. Federated Learning (FL) is a game-changing technology conceived to address the growing data privacy concern by moving training from the datacentre to user devices at the network edge, allowing sensitive data to remain on the devices where it was generated. FL has enormous potential for real-world, privacy-sensitive applications such as autonomous driving, diagnostic healthcare, and predictive maintenance.The operating environment for FL at the edge is extremely challenging for a variety of reasons: 1) the data owned by FL clients is highly heterogeneous (in regard to data distribution, quality, and quantity) and dynamic (data distributions change over time); 2) the hardware devices have diverse computing and communication capabilities with stringent resource constraints (e.g., battery power); and 3) FL clients work under unreliable wireless edge network conditions. Hence, despite FL's huge promise, there are considerable barriers to its wider real-world adoption for mission-critical AI applications that need real-time, on-demand responses, caused by several grand challenges: Challenge 1) lack of FL algorithms delivering consistent performance for dynamic client data, diverse client hardware, and unreliable wireless connections simultaneously; Challenge 2) lack of rigorous theoretical analyses of real-time, real-world FL algorithms; Challenge 3) lack of optimised, energy-efficient, versatile hardware acceleration for real-time FL.To address these important challenges, this project will create revolutionary algorithm-hardware co-design approaches to make FL a real-time process with unparalleled speed, performance, and energy-efficiency at the wireless edge, capable of meeting the stringent requirements of mission-critical applications. This research will pioneer a set of original methods and innovative technologies including: 1) disruptive lightweight hardware-aware FL algorithms that significantly reduce communication, computing, and energy costs while achieving fast model updates; 2) rigorous mathematical analyses of the proposed algorithms to prove their convergence rates and offer theoretical insights into how they perform under various edge network conditions; 3) an automatic hardware-software co-optimisation framework integrating specialised training-acceleration and power-reduction methods to realise optimised, energy-efficient hardware acceleration; and 4) a unique prototype system that will integrate the designed FL hardware accelerator and real-time FL algorithms and be evaluated in a realistic wireless edge networking testbed.This project has the potential to transform FL from a lengthy and disjointed process to a continuous, real-time procedure with concurrent model training and deployment. The proposed research will contribute to the UK's digital transformation and green economy by creating ground-breaking technologies for creating innovative AI-empowered products with significantly improved performance and energy-efficiency while complying with strict data-privacy protection.

过去几年见证了迅速增长的无线连接设备，例如智能手机和图像互联网（IoT）设备，这些设备产生了越来越多的数据驱动数据驱动密钥人工智能（AI）应用程序。但是，用户越来越不愿意将其私人数据（例如媒体，位置或传感器数据）上传到中心位置（例如，云数据中心）进行培训机器学习（ML）模型，以及诸如2018年数据保护法的数据保护法律越来越限制数据使用。联合学习（FL）是一种改变游戏规则的技术，旨在通过将培训从数据中心移动到网络边缘的用户设备，从而解决了不断增长的数据隐私问题，从而使敏感数据保留在生成的设备上。 FL具有对现实世界，对隐私敏感的应用的巨大潜力，例如自动驾驶，诊断性医疗保健和预测性维护。由于多种原因，FL的FL的运营环境极具挑战性：1）FL客户拥有的数据是高度异构的（与数据分配，质量和质量，质量和动态分布相关（数据分配）和动态分布（数据分布）更加多样化（数据分布）； 2）硬件设备具有不同的计算和通信功能，并具有严格的资源限制（例如，电池电源）； 3）FL客户在不可靠的无线边缘网络条件下工作。因此，尽管FL具有巨大的希望，但其对关键任务的AI应用程序的更广泛的现实采用存在很大的障碍，这些应用需要实时，按需响应，这是由于几个巨大的挑战而引起的：挑战：挑战1）缺乏FL算法为动态的客户数据，多样的客户硬件和无关无限的连接提供一致的FL算法，挑战2）缺乏实时，现实世界中FL算法的严格理论分析；挑战3）缺乏针对这些重要挑战的优化，节能，多功能的硬件加速度来解决这些重要的挑战，该项目将创建革命性的算法 - 硬件联合设计方法，以使FL以无与伦比的速度，性能和能源效率在无线边缘处于实时过程中，并能够满足任务严格应用程序的严格要求。这项研究将开创一系列原始方法和创新技术，包括：1）破坏性的轻型硬件感知的FL算法，这些算法会大大降低沟通，计算和能源成本，同时实现快速模型更新； 2）对所提出的算法进行严格的数学分析，以证明其收敛速率并提供理论上的见解，以了解它们在各种边缘网络条件下的性能； 3）自动硬件 - 软件合作框架框架集成了专业的培训加速器和功率还原方法，以实现优化的，节能的硬件加速度； 4）一个独特的原型系统，它将集成设计的FL硬件加速器和实时FL算法，并在现实的无线边缘网络测试中进行评估。该项目有可能将FL从漫长而脱节的过程转换为连续的实时过程，并通过同一模型培训和部署。拟议的研究将通过创建开创性的技术来创建具有显着提高的性能和能源效率的创新能力的产品，同时遵守严格的数据私人保护保护，从而为英国的数字转型和绿色经济做出贡献。