Heart disease is a leading global cause of death, while federated learning (FL) is an effective way to predict it. Due to patient privacy concerns and the centralized nature of current FL approaches, collaborative research in heart disease prediction (HDP) faces significant hurdles in computational health systems. This article introduces a distributed online aggregation method within fully decentralized federated learning (DFL), named DeFedHDP, to address these privacy challenges and improve the model of HDP. Moreover, the differential privacy (DP) mechanism is applied to the aggregation strategy of DeFedHDP to protect the privacy of the patients. The data holder communicates directly with neighbors in a series of time-varying directed graphs without the involvement of a central server. Furthermore, each participant is both a trainer of the local model and a collaborator of the other participants’ models. The data does not leave the local device, only the model parameters are exchanged and integrated, and this decentralized approach can further improve the level of privacy protection. In addition, to cope with model gradient disappearance and gradient explosion, the one-point bandit feedback (OPBF) strategy is utilized to estimate the true gradient values. Experiments on a public medical dataset show that the effectiveness of DeFedHDP is close to the centralized FedAVG algorithm for client-server architectures in terms of accuracy and speed.

Fault analysis activity is very important for a flexible manufacturing cell (FMC) in the development phase. Fault tree analysis (FTA) and fault mode and effects analysis (FMEA) are widely used for fault analysis. However, they are time-consuming and expensive when fully implemented. In this paper, we propose an improving hybrid multilevel FTA-FMEA method that is the combination of the above two methods. The proposed method has a clear three-layer analysis structure. In the first layer, a system FTA of the FMC is performed to determine the functional fault modes. Then, FMEA is conducted to examine them and the key functional fault modes are selected by criticality analysis. In the second layer, we perform the FTA of the determined key functional fault modes to find out the meta-action/component fault modes. The bottom events of fault trees in this layer show differences due to the subsystems with different features. Same as the first layer, FMEA is conducted subsequently and criticality analysis is also used to determine the key meta-action/component fault modes. In the last layer, we perform the FTA of the determined key meta-action/component fault modes to find out fault causes. Then, the key fault causes are determined by criticality analysis. Risk priority number (RPN) is usually used to determine the priority ranking in criticality analysis, while its calculation way is slightly naïve. In this paper, we use the technique for order preference by similarity to ideal solution (TOPSIS) method to examine the priority ranking of fault modes/causes. Moreover, we consider the correction cost as the fourth indicator to assess the priority. The improving fault analysis method can not only help designers better understand the new FMC but also help decision makers make better decisions. At last, a real FMC as a case is presented to illustrate the proposed method.