Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications.

Federated learning (FL) has been used to protect data contributors’ privacy by allowing training at clients and then feeding back machine learning models to servers for aggregation. Conventional methods of FL aggregation methods average model weights to produce a fused global model. However, in real-world applications in cyber-space systems, which often have heterogeneous Internet of Things data configuration and collection, FL encounters obstacles with non-independent and identically distributed (Non-IID) data. The main problem is the aggregated global models deviating from the optimal model trained on centralized servers. According to recent research, most Non-IID FL aggregation methods attempt to direct the movement of gradients to the optimal one using differentiation from trained models. In this paper, we propose a framework for using feature probability distribution in aggregation calculation. The proposed aggregation algorithm shows robustness on different Non-IID datasets and outperforms state-of-the-art methods in various FL experiments.

Due to the rapid recent development of cloud-edge networks, smart devices can facilitate rapid access to patients’ health information. Success has been achieved in the healthcare sector with the training of a federated learning (FL) model on large amounts of the personal data of users. However, some challenges remain that other FL models have not yet addressed. Firstly, FL models with computational parameters are very complex, which results in a high communication cost in the cloud-edge network. Furthermore, trained models in the cloud are not personalized. If personalization is present, the models do not provide practical solutions to fine-tune parameters in order to accurately predict performance in health monitoring. To address the above challenges, this paper presents the Fed xData framework for contextual health monitoring in cloud-edge networks. The Fed xData framework introduces a continuous data balancing supplemented structure using the RandomOverSample method, which solves all data classes. The FL model is an encode depth convolutional network (EDCN) model designed for both server and client. It solves various problems, for instance by using the fine-tuning model to increase personalization and solving not independent and identically (Non-IID) distribution problems regarding user health. Test results based on human activity recognition indicate that Fed xData is far superior to others for use in general centralized learning models and FL models.

Air pollution prediction for smart city applications has been attracted in artificial intelligence research to overcome problems to the health of citizen. Conventionally, environmental IoT data is gathered from monitoring station sensors then is sent to servers for centralized predictive model training at a whole region. That causes latency issues in data transmission from IoT devices to cloud servers. This paper describes federated learning paradigm approach for air pollution prediction model training on environmental monitoring sensor data. In the research, we design distributed learning framework that assists cooperative training among participants from different spatial areas such as cities and prefectures. At each area, Convolutional Recurrent Neural Networks (CRNN) are trained locally aiming to predict local Oxidant warning level while aggregated global model enhances distilled knowledge from all areas of a region. The research illustrates that designed common parts of CRNN can be fused globally meanwhile adaptive structure at predictive part of the deep neural network model can capture different environmental monitoring stations configuration at local areas. Some experiment results also hint methods to keep balance between federated learning synchronous training rounds and local deep neural network training epochs to maximize accuracy of the whole federated learning system. The results also prove that new participating areas can train and quickly obtain optimized local models by using transferred common global model.