Edge computing is a key enabling technology that meets continuously increasing requirements for the intelligent Internet of Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attackers well-crafted local updates. In this paper, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GAN). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Secondly, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions, and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two-types of typical poisoning attack strategies, label-flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments but also other entities’ policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next-generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in the future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues. Finally, we discuss the challenges, open issues, and future directions related to the applications of MARL in the future Internet.

Traffic flow prediction has received rising research interest recently since it is a key step to prevent and relieve traffic congestion in urban areas. Existing methods mostly focus on road-level or region-level traffic prediction, and fail to deeply capture the high-order spatial-temporal correlations among the road links to perform a road network-level prediction. In this paper, we propose a network-scale deep traffic prediction model called TrafficGAN, in which Generative Adversarial Nets (GAN) is utilized to predict traffic flows under an adversarial learning framework. To capture the spatial-temporal correlations among the road links of a road network, both Convolutional Neural Nets (CNN) and Long-Short Term Memory (LSTM) models are embedded into TrafficGAN. In addition, we also design a deformable convolution kernel for CNN to make it better handle the input road network data. We extensively evaluate our proposal over two large GPS probe datasets in the arterial road network of downtown Chicago and Bay Area of California. The results show that TrafficGAN significantly outperforms both traditional statistical models and state-of-the-art deep learning models in network-scale short-term traffic flow prediction.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining i ) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame, and ii ) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often includes sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This paper begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the paper thoroughly describes and compares the existing defense methods. Finally, the paper concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders.

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in smart IoT systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, lots of privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constraint nature of smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable privacy-preserving data aggregation scheme, named LVPDA, for the edge computing enabled IoT system, where the Paillier homomorphic encryption method and online/offline signature technique are combined to ensure the privacy-preserving and integrity verification during the data aggregation process. Detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under q-Strong Diffie-Hellman (q-SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight privacy-preserving data aggregation in terms of less computational complexity and communication overhead.