Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Ben shu jiang Dunhuang seng si na ru dao zheng ge zhong gu she hui fo jiao fa zhan de zhe ge da de li shi bei jing dang zhong, yi Dunhuang jing tu si he long xing si wei chong dian, yan jiu Tang hou qi Wu dai Song chu Dunhuang seng si de cun zai xing tai. 本书将敦煌僧寺纳入到整个中古社会佛教发展的这个大的历史背景当中, 以敦煌净土寺和龙兴寺为重点, 研究唐后期五代宋初敦煌僧寺的存在形态.

Buddhism --- Buddhism. --- Buddhist monasteries --- Buddhist monasteries. --- Fo jiao --- History --- Si yuan --- Yan jiu --- 581-960. --- China --- China. --- Dun huang (li shi di ming) --- Fo jiao shi --- Yan jiu.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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