Federated learning is a new distributed privacy-preserving learning paradigm which perfectly meets the requirements of many large service systems such as banking, healthcare, and smart city. Meanwhile, person re-identification, as a technology to associate the images of the same person from different data sources, has been widely used in many smart services such as smart logistics, smart surveillance, and many public searching and rescue missions. Therefore, it is a promising solution to use federated learning for person re-identification to improve the model accuracy while protecting the data privacy. However, the common problem of non-independent and identically distributed (Non-IID) data with heterogeneous clients in federated learning often causes undesirable model accuracy. To address such a problem, in this paper, we propose a novel strategy named federated learning with data augmentation for person re-identification (Fed4ReID). Specifically, to alleviate the impact of Non-IID data, we utilise a pre-trained DCGAN (Deep Convolutional Generative Adversarial Network) model for data augmentation at each edge servers. Experiments on public datasets show that our proposed strategy can outperform baseline method in general accuracy.

The Unmanned Aerial Vehicles (UAVs) delivery service is being increasingly used in logistics. However, it is challenging for a UAV to precisely identify the position for parcel delivering if it is only aided by the GPS, especially in some complex environments with weak signals and high interference. For this issue, we present a knowledge distillation empowered edge intelligence architecture, KeepEdge, to achieve visual information-assisted positioning for the UAV delivery services. Specifically, we integrate deep neural networks (DNN) into an edge computing framework to enable edge intelligence which empowers the UAVs to autonomously identify the expected delivery position. Deploying the DNN model and conducting model inference on UAVs however, requires high computing performance. To manage the trade-off between the limited resources onboard the UAVs and high-performance requirements, we employ knowledge distillation to produce a lightweight model with high accuracy based on the full model trained in the cloud. The lightweight model with significantly lower complexity and less inference latency is used onboard of the UAVs for accurate positioning. Comprehensive experiments show that the proposed architecture achieves satisfactory performance for assisted positioning. A real-world case study is presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.

AI (Artificial Intelligence) technology has been widely used in smart systems which usually require computing services with high availability and fast response. However, the rapid growth of data and service requests generated by end devices brings critical challenges to the centralised cloud computing paradigm in terms of network bandwidth, reliability and response time. In addition, the problem of data privacy is arising due to a large amount of data being transferred to the cloud server. Recently, edge computing is becoming a popular platform for smart systems as its provisions computing services close to the end devices, and Federated Learning (FL) is emerging as a promising solution for AI applications to address the data privacy issue. Inspired by their success, in this paper, we propose an edge based FL framework named Fed-UAV to solve the person reidentification problem in the UAV delivery service which is a typical AI application in smart logistics. This framework enables the UAV to efficiently locate the target receivers, and effectively reduce the data transmission between the UAV and the cloud server to improve the response time and protect the data privacy. Comprehensive experiments are conducted on three real-world datasets, and the experimental result successfully demonstrates that Fed-UAV can achieve both high accuracy and efficiency in person re-identification while protecting data privacy.

As an ideal infrastructure for smart services, Fog Computing is becoming the next wave of IT investment harnessing the successful models of Cloud Computing and latest technologies such as 5G and Internet of Things (IoT). However, the development of Fog Computing systems is a big challenge due to its complex, heterogeneous and distributed nature. Currently, there are a few SDKs released by some public Cloud service providers to support the development of Fog services in a top-down fashion as the key motive is to leverage their business Cloud services. However, Fog Computing systems are usually designed in a bottom-up fashion as the major functionalities are centred around the Edge Nodes and the End Devices. Meanwhile, significant efforts are required to verify the conformance of software behaviours as the collaboration between the End Devices, Edge Nodes and Cloud Servers is vital to the success of a Fog Computing System. Therefore, a holistically designed software development platform is urgently required. In this paper, we propose TDD4Fog, a test-driven software development platform for Fog Computing systems. Following the Test-Driven Development (TDD) methodology and a bottom-up design fashion, TDD4Fog supports the microservice architecture and provides the Test-Driven utilities such as metamorphic testing, mutation testing and random testing for the whole software development lifecycle of Fog Computing systems. To demonstrate the feasibility of TDD4Fog, we have presented some preliminary results on the key components of TDD4Fog and discussed some important future research directions.