Cognitive Data Fusing for Internet of Things Based on Ensemble Learning and Federated Learning

Big data produced by Internet of Things (IoT) devices is the key drive for prognostic and health management (PHM) for industrial equipment or systems. However, data are usually distributed stored in many scenarios due to security and privacy problems. Federated learning (FL) is an effective solution to fuse the data for intelligent decision. But FL faces risk of Denial-of-Service (DoS) attack or single-point-of-failure (SPOF) problem during training and service phases, and exchange of model parameters poses heavy network traffic between clients. Ensemble learning (EL) is widely used to boost task performance by combining diverse base learners, and it has shown promise in improving distributed intelligent services. Since a decision is collaboratively made by multiple clients in EL in a distributed fashion, DoS and SPOF problem can be inherently avoided, and the deployment cost is much lower than FL. Based on these good properties, we proposed to combine FL and EL for distributed IoT data fusion with a cognitive approach. First, we propose to construct effective EL by generating diverse base models with advanced pruning method and compare the performance of FL- and EL-based distributed data fusion. Then, a hierarchical combination of FL and EL is proposed based on the cognition of cost and performance at each level for efficient deployment of distributed IoT data fusion. Experimental results show that the EL-based scheme can achieve close performance to the FL-based scheme for small number of clients with some data sharing, and the cognitive hierarchical combination of FL and EL can achieve a good tradeoff between task performance and network traffic for large-scale distributed IoT data fusion.