Full leverage of the huge volume of data generated on a large number of user devices for providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to developing UI lies in the involvement of the large number of network devices, which contribute their data to collaborative Machine Learning (ML) and provide their computational resources to support the learning process. Federated Learning (FL) is a new ML method that enables data owners to collaborate in model training without exposing private data, which allows user devices to contribute their data to developing UI. Edge computing deploys cloud-like capabilities at the network edge, which enables network devices to offer their computational resources for supporting FL. Therefore, a combination of FL and edge computing may greatly facilitate the development of ubiquitous intelligence in the 6G network. In this article, we present a comprehensive survey of the recent developments in technologies for combining FL and edge computing with a holistic vision across the fields of FL and edge computing. We conduct our survey from both the perspective of an FL framework deployed in an edge computing environment (FL in Edge) and the perspective of an edge computing system providing a platform for FL (Edge for FL). From the FL in Edge perspective, we first identify the main challenges to FL in edge computing and then survey the representative technical strategies for addressing the challenges. From the Edge for FL perspective, we first analyze the key requirements for edge computing to support FL and then review the recent advances in edge computing technologies that may be exploited to meet the requirements. Then we discuss open problems and identify some possible directions for future research on combining FL and edge computing, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field.

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid development of Internet of Things (IoT) and cloud technologies together with recent advances in machine learning offered various cloud-based data-driven approaches to automatic anomaly detection. However, the challenges introduced by the different types of equipment in rail transit systems with highly diverse data distributions and the lack of labeled anomaly data have not been sufficiently addressed. In this article, we attempt to cope with such challenges by proposing an improved long short term memory (LSTM)-based time-series anomaly detection scheme. The key elements of the proposed scheme include an improved LSTM model that may achieve more accurate time-series prediction for various rail transit devices and a method for determining an appropriate error threshold for detecting anomalies based on the prediction errors. In order to further enhance anomaly detection performance, we also propose a pruning algorithm for reducing the number of false anomalies. Our method does not rely on scarce anomaly labels but dynamically determines a threshold of prediction errors to identify anomalies; therefore, it overcomes the challenge of the extremely uneven distribution of rail transit data. We conducted extensive experiments in a real metro operation environment for performance evaluation. The experiment results prove the effectiveness of the proposed scheme and show a superior performance of the scheme compared to existing anomaly detection methods.

Edge-cloud hybrid environment requires complex deployment strategies to enable the smart IoT system. However, current service deployment strategies use simple, generalized heuristics and ignore the heterogeneous characteristics in edge-cloud hybrid environment. In this paper, we devise a method to find a microservice-based service deployment strategy that can reduce the average waiting time of IoT devices in hybrid environment. For this purpose, we first propose a microservice-based deployment problem (MSDP) based on the heterogeneous and dynamic characteristics in edge-cloud hybrid environment, including heterogeneity of edge server capacities, dynamic geographical information of IoT devices, changing device preference for applications and complex application structures. We then propose a Multiple Buffer Deep Deterministic Policy Gradient (MB_DDPG) to provide more preferable service deployment solutions. Our algorithm leverages reinforcement learning and neural network to learn a deployment strategy without any human instruction. Therefore, service provider can make full use of limited resources to improve quality of service(QoS). Finally, we implement MB_DDPG based on real-world datasets and some synthetic data, we also implement another two algorithms, genetic algorithm and random algorithm, as a contrast. The experimental results demonstrate that MB_DDPG is able to learn a preferable strategy which, in terms of average waiting time, outperforms genetic algorithm and the random algorithm by 32% and 44%, respectively.

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community’s interest in this emerging field.