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This book constitutes the refereed proceedings of the 5th International Conference on Decision and Game Theory for Security, GameSec 2014, held in Los Angeles, CA, USA, in November 2014. The 16 revised full papers presented together with 7 short papers were carefully reviewed and selected from numerous submissions. The covered topics cover multiple facets of cyber security that include: rationality of adversary, game-theoretic cryptographic techniques, vulnerability discovery and assessment, multi-goal security analysis, secure computation, economic-oriented security, and surveillance for security. Those aspects are covered in a multitude of domains that include networked systems, wireless communications, border patrol security, and control systems.

Complex analysis --- Operational research. Game theory --- Mathematics --- Computer science --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- draadloze computernetwerken --- complexe analyse (wiskunde) --- bedrijfssoftware --- computers --- informatica --- informatiesystemen --- speltheorie --- wiskunde --- computerbeveiliging --- informatica management --- computernetwerken --- computerkunde

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This brief introduces overlapping coalition formation games (OCF games), a novel mathematical framework from cooperative game theory that can be used to model, design and analyze cooperative scenarios in future wireless communication networks. The concepts of OCF games are explained, and several algorithmic aspects are studied. In addition, several major application scenarios are discussed. These applications are drawn from a variety of fields that include radio resource allocation in dense wireless networks, cooperative spectrum sensing for cognitive radio networks, and resource management for crowd sourcing. For each application, the use of OCF games is discussed in detail in order to show how this framework can be used to solve relevant wireless networking problems. Overlapping Coalition Formation Games in Wireless Communication Networks provides researchers, students and practitioners with a concise overview of existing works in this emerging area, exploring the relevant fundamental theories, key techniques, and significant applications. .

Operational research. Game theory --- Mathematics --- Telecommunication technology --- Mass communications --- Computer architecture. Operating systems --- draadloze computernetwerken --- tekstverwerking --- speltheorie --- wiskunde --- computernetwerken --- communicatietechnologie

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Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

Programming --- Computer architecture. Operating systems --- programmeren (informatica) --- computernetwerken