The traditional federated learning (FL) approach in the Wireless Internet of Things (WIoT) relies on a central server to coordinate and manage the entire training process, which contradicts the design concept of distributed and autonomous IoT systems. In actual WIoT scenarios, it is difficult to find a reliable third party to serve as a central server to reduce the risk of single points of failure and system paralysis. Moreover, the dynamism and heterogeneity of the WIoT system require strong privacy protection mechanisms to safeguard the vast amounts of user data stored on devices. To tackle these issues, this paper introduces a decentralized federated learning (DFL) utilizing differential privacy (DP) applied in WIoT scenarios to ensure robust privacy protection. Furthermore, to address the black box model problem of FL neural networks, a one-point residual feedback (OPRF) mechanism is adopted. Unlike one-point feedback, regret analysis in OPRF relies on weaker assumptions and is more applicable to practical non-stationary optimization problems. Although OPRF increases randomness, complicating convergence boundaries and potentially causing error accumulation, rigorous analysis demonstrates that the proposed algorithm achieves a sublinear rate and$\epsilon$-differential privacy. Finally, simulation experiments validate the effectiveness and superior performance of the algorithm.

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.

The economic dispatch problem(EDP) in microgrid over directed networks with communication time delays is investigated. We design a distributed event-based algorithm to handle EDP in microgrid over strongly connected and balanced digraph. Furthermore, the effect of time delays has been considered. By graph theory and Lyapunov method, an appropriate event-triggered condition is obtained and the corresponding sufficient condition which guarantees stability of the proposed algorithm is devised. As the periodic sample control has been adopted, the Zeno behavior is excluded naturally. Finally, Simulation results demonstrate the effectiveness of the algorithm.

By combining the Newton-Raphson method, the graph discovery algorithm, and the finite-time consensus algorithm, a novel iterative algorithm is proposed in this paper to solve the economic dispatch problem of modern cyber-physical power systems. Fully distributed implementation and finite-time convergence properties distinguish our algorithm from the existing iterative algorithms. Several numerical examples are applied to illustrate our design procedure and validate our proposed algorithm.

Communication networks are increasingly applied in smart grid for information exchange and optimal operation. This paper presents an optimal active power control strategy for islanded microgrid in the presence of communication delays. The control strategies are fully distributed. The impact of the diverse communication delays and the effectiveness of the algorithm are analyzed by using the Generalized Nyquist criterion. The proposed approach can minimize the total cost and maintain the supply-demand balance of the microgrid. Several case studies are carried out to verify the effectiveness of the proposed approach.