Federated learning (FL) has developed rapidly in recent years as a privacy-preserving machine learning method, and it has been gradually applied to key areas involving privacy and security such as finance, medical care, and government affairs. However, the current solutions to FL rarely consider the problem of migration from centralized learning to federated learning, resulting in a high practical threshold for federated learning and low usability. Therefore, we introduce a reliable, efficient, and easy-to-use federated learning framework named Neursafe-FL. Based on the unified application program interface (API), the framework is not only compatible with mainstream machine learning frameworks, such as Tensorflow and Pytorch, but also supports further extensions, which can preserve the programming style of the original framework to lower the threshold of FL. At the same time, the design of componentization, modularization, and standardized interface makes the framework highly extensible, which meets the needs of customized requirements and FL evolution in the future. Neursafe-FL is already on Github as an open-source project ¹ .

Optical wireless communications have been widely studied during the past decade in short-range applications, such as indoor high-speed wireless networks and interconnects in data centers and high-performance computing. In this paper, recent developments in high-speed short-range optical wireless communications are reviewed, including visible light communications (VLCs), infrared in-door communication systems, and reconfigurable optical interconnects. The general architecture of indoor high-speed optical wireless communications is described, and the advantages and limitations of both visible and infrared based solutions are discussed. The concept of reconfigurable optical interconnects is presented, and key results are summarized. In addition, the challenges and potential future directions of short-range optical wireless communications are discussed.