Recent advances on federated learning systems and the design for computing power Internet of things - LU Jianfeng; QI Pan; PAN Linyu; LI Bing; CAO Shuqin; JIN Yan'an (物联网学报)
Computing power Internet of things (CPIoT) integrates Internet of things (IoT) devices with substantial computational resources to support data-intensive tasks, facilitating intelligent decision-making. Within the context of privacy protection requirements for CPIoT, federated learning (FL) that is a distributed learning technique upholds data privacy, and offers a novel approach to addressing data silos for executing complex training tasks, and training large models. Although researchers have been committed to develop more mature federated learning systems to adapt to the CPIoT environment, current research lacks in-depth exploration of the strengths and limitations, technical features and differences, and support and applicability of federated learning system design techniques. Firstly, the most influential federated learning systems in the industry were studied, including open-source frameworks and benchmarking platforms. The system design differences in various technical dimensions of CPIoT in an in-depth comparison were analyzed. Detailed criteria and recommendations for selecting open-source frameworks and benchmarking platforms in the CPIoT environment were established, so that developers could efficiently choose the most suitable frameworks and platforms. Seeondly, various experiments for selecting federated learning systems and building complete systems were presented in multiple CPIoT scenarios, to assist developers in better realizing federated learning applications by utilizing the aforementioned technologies. Finally, the current state of standardization and development challenges in the field of federated learning system design were summarized, and future development prospects were discussed. The purpose is to provide a comprehensive overview of FL systems and the design research progress, serving as a reference for the deep integration of CPIoT and FL networks and offering insights for future research.