Facing the upcoming era of Internet-of-Things and connected intelligence, efficient information processing, computation and communication design becomes a key challenge in large-scale intelligent systems. Recently, Over-the-Air (OtA) computation has been proposed for data aggregation and distributed function computation over a large set of network nodes. Theoretical foundations for this concept exist for a long time, but it was mainly investigated within the context of wireless sensor networks. There are still many open questions when applying OtA computation in different types of distributed systems where modern wireless communication technology is applied. In this article, we provide a comprehensive overview of the OtA computation principle and its applications in distributed learning, control, and inference systems, for both server-coordinated and fully decentralized architectures. Particularly, we highlight the importance of the statistical heterogeneity of data and wireless channels, the temporal evolution of model updates, and the choice of performance metrics, for the communication design in OtA federated learning (FL) systems. Several key challenges in privacy, security and robustness aspects of OtA FL are also identified for further investigation.
翻译:面对即将到来的互联网和互联情报时代,高效的信息处理、计算和通信设计成为大规模智能系统中的一项关键挑战。最近,提议对大量网络节点进行数据汇总和分布函数计算,为此概念的理论基础存在很长时间,但主要在无线传感器网络范围内调查。在应用现代无线通信技术的不同分布式系统应用OTA计算时,仍然有许多未决问题。在本条中,我们全面概述了OtA计算原则及其在分布式学习、控制和推断系统中的应用,包括服务器协调和完全分散的架构。我们特别强调数据和无线频道的统计多样性、模型更新的时程演变和性能衡量标准的选择对于OtA联邦化学习系统的通信设计的重要性。还确定了OtA FL的隐私、安全和稳健性方面的几个重大挑战,以供进一步调查。