The rapid expansion of Electric Internet of Things (EIoT) systems has led to an increased demand for accurate and secure methodologies capable of handling short-term energy information forecasting, which is crucial for optimizing energy management and maintaining smart grid stability. Current federated learning (FL) frameworks, however, struggle to cope with the inherent heterogeneity of devices and the constrained communication environments typical in EIoT settings. To overcome these challenges, we introduce the federated heterogeneity-aware adaptive framework (FedHA), a novel approach that combines an adaptive asynchronous federated aggregation (AAF) framework with a hierarchical knowledge distillation (HKD) strategy. The AAF framework reduces global model aggregation time and minimizes the impact of straggling clients by enabling parallel training and dynamically adjusting learning rates based on client participation. Concurrently, HKD improves the stability and consistency of model convergence, particularly in non-IID data scenarios, by distilling and leveraging knowledge from clients that most accurately represents the global gradient direction. Furthermore, we developed FedHAL, a light-weight version of FedHA, which optimizes communication efficiency, making it ideal for deployment in resource-constrained edge environments. Extensive experiments on real-world EIoT datasets confirm that FedHA outperforms existing FL algorithms in terms of both prediction accuracy and adaptability, underscoring its potential for widespread application in dynamic and diverse EIoT contexts.

As renewable energy resources increasingly integrate and dynamic user loads emerge, precise short-term forecasting across various scenarios becomes crucial for the efficient operation of modern power systems. Federated learning (FL) presents an effective solution for forecasting that safeguards privacy and security through model aggregation without sharing sensitive raw data. In the Electric Internet of Things (EIoT), the heterogeneity of clients can complicate model convergence, challenging the efficiency of the current federated averaging (FedAvg)-based methods. To address this, our analysis of regression challenges within the EIoT and the unique attributes of FL methods has led to the development of a novel algorithm: federated mutual knowledge distillation learning for regression (FedMR), aiming to deliver superior forecasting precision and efficiency at a reduced cost. FedMR utilizes the models and min-max labels from distributed clients, enabling its embedded generator to assimilate global knowledge and synthesize data in a normal distribution pattern. This feature enhances training across a spectrum of user environments. Demonstrated through testing on three open EIoT data sets, FedMR showcases high efficiency, accuracy, and adaptability across diverse client participation scenarios. Additionally, our research delves into the application of FedMR in managing device heterogeneity, a common issue in the EIoT systems. By focusing on sharing only the regression layers of clients, our proposed FedMRL(ightweight) pioneers a model-agnostic aggregation approach in short-term forecasting, effectively achieving a harmonized balance of accuracy and efficiency.

Algorithms for signal denoising that combine wavelet-domain sparsity and total variation (TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations, normally introduced by pure wavelet thresholding. This paper formulates wavelet-TV (WATV) denoising as a unified problem. To strongly induce wavelet sparsity, the proposed approach uses non-convex penalty functions. At the same time, in order to draw on the advantages of convex optimization (unique minimum, reliable algorithms, simplified regularization parameter selection), the non-convex penalties are chosen so as to ensure the convexity of the total objective function. A computationally efficient, fast converging algorithm is derived.

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No abstract available.