A federated learning scheme meets dynamic differential privacy

Abstract Federated learning is a widely used distributed learning approach in recent years, however, despite model training from collecting data become to gathering parameters, privacy violations may occur when publishing and sharing models. A dynamic approach is proposed to add Gaussian noise more effectively and apply differential privacy to federal deep learning. Concretely, it is abandoning the traditional way of equally distributing the privacy budget ϵ and adjusting the privacy budget to accommodate gradient descent federation learning dynamically, where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually. It also incorporates adaptive threshold cropping to control the sensitivity, and finally, moments accountant is used to counting the ϵ consumed on the privacy‐preserving, and learning is stopped only if the ϵtotal by clients setting is reached, this allows the privacy budget to be adequately explored for model training. The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy, which is significantly better than the differential privacy method used by TensorFlow

A water breakthrough warning system of high-yield wells in fracture-cavity reservoirs in Tahe Oilfield

A water-out warning system is set up by using water breakthrough data of high yield wells, summing up warning parameters of water-out, and combining with influencing factors of water-out. During the development of Ordovician karst-cave and fracture-vug reservoirs in the Tahe Oilfield, bottom water goes through four stages: prior water invasion, cone period, supporting cone period and breakthrough period. In the late period of the supporting cone, bottom water leads to pressure oscillation in the dissolved cave system, which affects the bottom-hole pressure to some extent. According to the nodal analysis theory, the change of bottom-hole pressure will lead to changes of wellhead pressure, and abnormal signals such as abnormal bottom-hole flowing pressure, tubing pressure, casing pressure, or output. According to geological, engineering, production management factors, and abnormal signals before water-out, 31 indices for water-out risk evaluation are summed up, and early water breakthrough warning technique for high-yield wells is established. Application of the technique to 146 no-water high-yield wells shows the warning technique works well and extends the water free production period. Key words: Tahe Oilfield, fractured-vuggy reservoir, high-yield well, abnormal signal, water-breakthrough warning mechanis

Genomewide Clonal Analysis of Lethal Mutations in the Drosophila melanogaster Eye: Comparison of the X Chromosome and Autosomes

Using a large consortium of undergraduate students in an organized program at the University of California, Los Angeles (UCLA), we have undertaken a functional genomic screen in the Drosophila eye. In addition to the educational value of discovery-based learning, this article presents the first comprehensive genomewide analysis of essential genes involved in eye development. The data reveal the surprising result that the X chromosome has almost twice the frequency of essential genes involved in eye development as that found on the autosomes