Privacy-preserving federated deep learning for cooperative hierarchical caching in fog computing

Over the past few years, Fog Radio Access Networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities towards the edge of networks, closer to users. In F-RANs, distributed edge caching among Fog Access Points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatio-temporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyse the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a Federated Learning based Cooperative Hierarchical Caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbour F-APs and vertical cooperation between the BaseBand Unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks

GRU with Dual Attentions for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices

Research on QoS-Based Networks Node-State

The selection and collection of the node state information have great impact on the design complexity and performance of a QoS routing algorithm. Continuous-media traffic (e.g. audio and video) can tolerate some loss but has rigid delay constraints. Under this QoS constraint, the node-delay's Probability Density Function (PDF) is used to character the node state. The continuous-media traffic is in high burst. So the nodedelay's PDF is shaped into Normal Distribution through a filter algorithm. Analysis and simulation show that the tail probability of the normal distribution can be used to calculate the end-to-end packet loss rate approximately. Then the variance and mean value of the PDF can be controlled to calculate a proper packet loss rate to satisfy the QoS demand. So normal distribution is a simple, achievable and reasonable description

GRU with Dual Attentions for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices

A secure collaborative spectrum sensing strategy in cyber-physical systems

Cyber-physical systems (CPS) have the great potential to transform people's lives. Smart cities, smart homes, robot assisted living, and intelligent transportation systems are examples of popular CPS systems and applications. It is an essential but challenging requirement to offer secure and trustworthy real-time feedback to CPS users using spectrum sharing wireless networks. This requirement can be satisfied using collaborative spectrum sensing technology of cognitive radio networks. Despite its promising benefits, collaborative spectrum sensing introduces new security threats especially internal attacks (i.e., attacks launched by internal nodes) that can degrade the efficiency of spectrum sensing. To tackle this challenge, we propose a new transferring reputation mechanism and dynamic game model-based secure collaborative spectrum sensing strategy (TRDG). More specifically, a location-aware transferring reputation mechanism is proposed to resolve the reputation loss problem caused by user mobility. Furthermore, a dynamic game-based recommendation incentive strategy is built to incentivize secondary users to provide honest information. The simulation experiments show that the TRDG enhances the accuracy of spectrum sensing and defends against the internal attacks effectively without relying on a central authority