Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal. To provide a comprehensive survey and facilitate the potential research of this area, we systematically introduce the existing works of federated learning from five aspects: data partitioning, privacy mechanism, machine learning model, communication architecture and systems heterogeneity. Then, we sort out the current challenges and future research directions of federated learning. Finally, we summarize the characteristics of existing federated learning, and analyze the current practical application of federated learning.

A survey on federated learning

Smart city and cyber-security; technologies used, leading challenges and future recommendations

A socio-technical analysis of China's cybersecurity policy: Towards delivering trusted e-government services

Smart cities and their internationalization process and efforts in order to gain the competitive advantage in the international arena have received a great deal of attention by marketing scholars and practitioners alike Yet, the growing number of studies focused on this topic has led to considerable fragmentation and theoretical confusion,To move the domain forward, this study applies the systematic review methodology and reviews 41 peer-reviewed articles published in highly esteemed publication outlets,Building on the antecedents–phenomenon–consequences framework, the authors discuss the antecedents and consequences of the various innovative marketing strategies that smart cities adopt for their internationalization and development of an international competitive advantage In the process of doing so, the authors synthesize the findings of the studies as well as literature gaps that provide fruitful avenues for future research,This article offers a systematic review of extant marketing research on smart cities and their efforts to internationalize In particular, this study advances the conceptual development of smart city internationalization and innovation by a marketing lens, provides an integrative, international-oriented framework that maps the extant literature across disciplines and countries, expands the boundaries of this research domain into new research paths and offers implications for policy and practice

Marketing innovation and internationalization in smart city development: a systematic review, framework and research agenda

Understanding the relationships between information architectures and business models: An empirical study on the success configurations of smart communities

Max Parasol

Papers

The impact of China's 2016 Cyber Security Law on foreign technology firms, and on China's big data and Smart City dreams

Avoiding the Wholesale De-banking of Cryptocurrency Exchanges in Australia