A Cross-Client Coordinator in Federated Learning Framework for Conquering Heterogeneity.

Federated learning, as a privacy-preserving learning paradigm, restricts the access to data of each local client, for protecting the privacy of the parties. However, in the case of heterogeneous data settings, the different data distributions among clients usually lead to the divergence of learning targets, which is an essential challenge for federated learning. In this article, we propose a federated learning framework with a unified coding space, called FedUCS, for learning cross-client uniform coding rules to solve the problem of divergent targets among multiple clients due to heterogeneous data.

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning.

Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent work to refine global models. However, these approaches rely on a proxy dataset or a data generator.

Migrate demographic group for fair Graph Neural Networks.

Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanilla GNNs, causing biased results toward particular demographic groups (divided by sensitive attributes, such as race and age).

Supplementary Feeding of Grazing Inner Mongolian Cashmere Goats during Pregnancy-Based on "Nutrient Requirements of Cashmere Goats".

This study aimed to conduct precise supplementation for pregnant cashmere goats under grazing based on the feeding standard. Eight Inner Mongolian pregnant cashmere goats of near-average body weight were selected at early gestation (44.41 ± 4.

The pure spin current and fully spin-polarized current induced by the photogalvanic effect and spin-Seebeck effect in halogen-decorated phosphorene.

The control of spin transport is a fundamental but crucial task in spintronics and realization of high spin polarization transport and pure spin currents is particularly desired. By combining the non-equilibrium Green's function with first principles calculations, it is shown that halogen adsorption can transform a black phosphorene monolayer from a nonmagnetic semiconductor to a magnetic semiconductor with two almost symmetric spin-split states near the Fermi level, which provides two isolated transport channels. Further investigations demonstrate that a device based on halogen-decorated phosphorene can behave multifunctionally, where a pure spin photocurrent and a fully spin-polarized photocurrent can be effectively controlled by tuning the photon energy or polarization angle of the incident light.

Comparative Study of the Nutritional Value and Degradation Characteristics of Amaranth Hay in the Rumen of Goats at Different Growth Stages.

The objective of this study was to investigate the rumen degradation characteristics of grain amaranth hay (Amaranthus hypochondriacus) at four different growth stages. The aim of this study was to evaluate the nutritional value of grain amaranth hay at different growth stages by chemical composition, in vivo digestibility, and in situ degradability. Three Boer goats with permanent ruminal fistulas were selected in this study.

Robust Corrupted Data Recovery and Clustering via Generalized Transformed Tensor Low-Rank Representation.

Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted tensor data and make it compatible with the downstream clustering task remains a challenging problem.

Effect of β-1,3/1,6-glucan on gut microbiota of yellow-feathered broilers.

β-1,3/1,6-glucan as a prebiotic improves immune performance in animals. These functions are closely related to the effect of β-1,3/1,6-glucan on gut microbiota structure. However, the effect of β-1,3/1,6-glucan on the gut microbiota structure of broilers is unclear.

Liver Transcriptome and Gut Microbiome Analysis Reveals the Effects of High Fructose Corn Syrup in Mice.

High fructose corn syrup (HFCS) is a viscous mixture of glucose and fructose that is used primarily as a food additive. This article explored the effect of HFCS on lipid metabolism-expressed genes and the mouse gut microbiome. In total, ten 3-week-old male C57BL/6J mice were randomly divided into two groups, including the control group, given purified water (Group C) and 30% HFCS in water (Group H) for 16 weeks.

Double Sparse Deep Reinforcement Learning via Multilayer Sparse Coding and Nonconvex Regularized Pruning.

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning.

Accelerated Partially Shared Dictionary Learning With Differentiable Scale-Invariant Sparsity for Multi-View Clustering.

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data.

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey.

Federated learning () is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation.

SHNE: Semantics and Homophily Preserving Network Embedding.

Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture.

Involves in the Modulation of Intestinal ANGPTLT4 Expression in Mice Treated by High-Fat Diet.

Increasing studies have shown that obesity is the primary cause of cardiovascular diseases, non-alcoholic fatty liver diseases, type 2 diabetes, and a variety of cancers. The dysfunction of gut microbiota was proved to result in obesity. Recent research indicated ANGPTL4 was a key regulator in lipid metabolism and a circulating medium for gut microbiota and fat deposition.

A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems.

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way.

Homophily Preserving Community Detection.

As a fundamental problem in social network analysis, community detection has recently attracted wide attention, accompanied by the output of numerous community detection methods. However, most existing methods are developed by only exploiting link topology, without taking node homophily (i.e.

Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster.

Medical assistance is crucial to disaster management. In particular, the situation of survivors as well as the environmental information after disasters should be collected and sent back to cloud/data centers immediately for further interpretation and analysis. Recently, unmanned aerial vehicle (UAV)-aided disaster management has been considered a promising approach to enhance the efficiency of searching and rescuing survivors after a disaster, in which a group of UAVs collaborates to accomplish the search and rescue task.

On Connectivity of Wireless Sensor Networks with Directional Antennas.

In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity.