Driven by privacy concerns and the visions of Deep Learning, the last four years have witnessed a paradigm shift in the applicability mechanism of Machine Learning (ML). An emerging model, called Federated Learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This paper starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions and trends in the literature including core system models and designs, application areas, privacy and security and resource management. Further, we discuss important challenges and open research directions towards more robust FL systems.

The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called Federated Learning. The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents.

As an alternative to centralized systems, which may prevent data to be stored in a central repository due to its privacy and/or abundance, Federated Learning (FL) is nowadays a game changer addressing both privacy and cooperative learning. It succeeds in keeping training data on the devices, while sharing locally computed then globally aggregated models throughout several communication rounds. The selection of clients participating in FL process is currently at complete/quasi randomness. However, the heterogeneity of the client devices within Internet of Things environment and their limited communication and computation resources might fail to complete the training task, which may lead to many discarded learning rounds affecting the model accuracy. In this paper, we propose FedMCCS, a multi-criteria based approach for client selection in FL. All of the CPU, Memory, Energy, and Time are considered for the clients resources to predict whether they are able to perform the FL task. Particularly, in each round, the number of clients in FedMCCS is maximized to the utmost, while considering each client resources and its capability to successfully train and send the needed updates. The conducted experiments show that FedMCCS outperforms the other approaches by (1) reducing the number of communication rounds to reach the intended accuracy, (2) maximizing the number of clients, (3) handling the least number of discarded rounds, and (4) optimizing the network traffic.

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals, such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices, such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multiparty architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing, and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: 1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog, and cloud computing layers; 2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of subtechniques; and 3) propose a comprehensive cybersecurity framework that combines the concepts of explainable artificial intelligence (XAI), federated learning, game theory, and social psychology to offer future IoT systems a strong protection against cyberattacks.

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.