Reliable federated learning in a cloud-fog-IoT environment

The paper presents RelFL, a Reliable Federated Learning system for collaborative and decentralized training of a deep learning model in a cloud-fog-Internet of Things (IoT) environment. Data generated by IoT devices is used at fog nodes for locally train a global deep learning model received from a cloud server. Further, a subset of reliable fog nodes is selected as the dominating set (DS) to act as local aggregators (LAs). A LA is responsible for aggregating its own locally trained model's weights with the weights shared by non-LA nodes in its vicinity. The locally aggregated weights are transferred by the LAs to the cloud server for updating the global model. The updated global model is then pushed back to the LAs, which transfer this model to non-LA nodes to start the next round of training. The selection of reliable fog nodes as LAs alleviates the risk of losing model updates due to fog nodes' failures. Results show that RelFL outperforms FedAvg, a widely established FL method, and its variant, FedProx in the presence of fog nodes' failures. RelFL also achieves the results of a centralized convolutional neural network (CNN) while preserving data privacy.