Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.
Collaborative learning has received huge interests due to its capability of exploiting the collective computing power of the wireless edge devices. However, during the learning process, model updates using local private samples and large-scale parameter exchanges among agents impose severe privacy concerns and communication bottleneck. In this paper, to address these problems, we propose two differentially private (DP) and communication efficient algorithms, called Q-DPSGD-1 and Q-DPSGD-2. In Q-DPSGD-1, each agent first performs local model updates by a DP gradient descent method to provide the DP guarantee and then quantizes the local model before transmitting it to neighbors to improve communication efficiency. In Q-DPSGD-2, each agent injects discrete Gaussian noise to enforce DP guarantee after first quantizing the local model. Moreover, we track the privacy loss of both approaches under the Renyi DP and provide convergence analysis for both convex and non-convex loss functions. The proposed methods are evaluated in extensive experiments on real-world datasets and the empirical results validate our theoretical findings.
Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR) used by AGMs varies significantly across the dimensions of the problem over epochs (i.e., anisotropic scale), which may lead to issues in convergence and generalization. All existing modified AGMs actually represent efforts in revising the A-LR. Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of Adam also depends on its hyper-parameter , which has been overlooked previously. Based on these two facts, we propose a new AGM by calibrating the A-LR with an activation (softplus) function, resulting in the Sadam and SAMSGrad methods. We further prove that these algorithms enjoy better convergence speed under nonconvex, non-strongly convex, and Polyak-Lojasiewicz conditions compared with Adam. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.
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