[论文解读] Towards Ubiquitous AI in 6G with Federated Learning

Yong Xiao, Guangming Shi+1 more|arXiv (Cornell University)|Apr 26, 2020

Privacy-Preserving Technologies in Data参考文献 13被引用 35

一句话总结

该论文综述了联邦学习如何在6G中实现普遍人工智能，提出了一种基于FL的架构，并概述了未来网络中大规模、隐私保护的分布式学习的挑战与研究方向。

ABSTRACT

With 5G cellular systems being actively deployed worldwide, the research community has started to explore novel technological advances for the subsequent generation, i.e., 6G. It is commonly believed that 6G will be built on a new vision of ubiquitous AI, an hyper-flexible architecture that brings human-like intelligence into every aspect of networking systems. Despite its great promise, there are several novel challenges expected to arise in ubiquitous AI-based 6G. Although numerous attempts have been made to apply AI to wireless networks, these attempts have not yet seen any large-scale implementation in practical systems. One of the key challenges is the difficulty to implement distributed AI across a massive number of heterogeneous devices. Federated learning (FL) is an emerging distributed AI solution that enables data-driven AI solutions in heterogeneous and potentially massive-scale networks. Although it still in an early stage of development, FL-inspired architecture has been recognized as one of the most promising solutions to fulfill ubiquitous AI in 6G. In this article, we identify the requirements that will drive convergence between 6G and AI. We propose an FL-based network architecture and discuss its potential for addressing some of the novel challenges expected in 6G. Future trends and key research problems for FL-enabled 6G are also discussed.

研究动机与目标

• 识别推动AI驱动的6G融合的关键需求。
• 提出一个基于FL的网络架构，使在大量异构设备之间实现分布式学习成为可能。
• 讨论将FL应用于解决6G在数据处理、隐私和可扩展性方面挑战的潜在应用。
• 强调面向FL-enabled 6G的未来趋势、未解问题与研究方向。

提出的方法

• 描述具备普遍人工智能的6G的高层愿景以及联邦学习的作用。
• 提出一种基于FL的架构，在该架构中设备向雾服务器注册以参与本地模型训练和全局聚合的轮次。
• 解释FL在6G环境下如何解决数据本地性、异质性、隐私和可扩展性。
• 讨论在6G框架内与AI-as-a-Service、HITL服务以及安全性考量的整合。

实验结果

研究问题

• RQ1 如何利用联邦学习在跨越大规模异构设备的6G中实现普遍人工智能？
• RQ2 在6G网络中，基于FL的哪种架构设计可以解决数据本地性、隐私、非IID数据以及可扩展性？
• RQ3 在实际部署FL支持的6G时，主要挑战与研究空白点是什么？

主要发现

• FL 使在分散数据上实现数据驱动的人工智能成为可能，同时保持数据在本地，降低原始数据传输。
• 基于FL的架构可以通过雾服务器协调设备更新和聚合来支持大规模参与。
• FL 可以处理非IID数据，并通过安全聚合和差分隐私等技术提供隐私保护。
• 在所提出的FL框架内，实现大规模聚合并与其他AI技术（深度学习、强化学习、迁移学习）集成是可行的。
• 本文指出实际挑战，如异构连接性、资源优化、具有性能保证的可解释性、因果推断以及以人为本的考量，并概述了未解决的研究主题。