Efficient Semi-Supervised Federated Learning for Heterogeneous Participants

Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data. However, training and deploying large-scale models on resource-constrained clients is challenging. Fortunately, Split Federated Learning (SFL) offers a feasible solution by alleviating the computation and/or communication burden on clients. However, existing SFL works often assume sufficient labeled data on clients, which is usually impractical. Besides, data non-IIDness across clients poses another challenge to ensure efficient model training. To our best knowledge, the above two issues have not been simultaneously addressed in SFL. Herein, we propose a novel Semi-SFL system, which incorporates clustering regularization to perform SFL under the more practical scenario with unlabeled and non-IID client data. Moreover, our theoretical and experimental investigations into model convergence reveal that the inconsistent training processes on labeled and unlabeled data have an influence on the effectiveness of clustering regularization. To this end, we develop a control algorithm for dynamically adjusting the global updating frequency, so as to mitigate the training inconsistency and improve training performance. Extensive experiments on benchmark models and datasets show that our system provides a 3.0x speed-up in training time and reduces the communication cost by about 70.3% while reaching the target accuracy, and achieves up to 5.1% improvement in accuracy under non-IID scenarios compared to the state-of-the-art baselines.Comment: 16 pages, 12 figures, conferenc

Association between chronic diseases and depression in the middle-aged and older adult Chinese population—a seven-year follow-up study based on CHARLS

BackgroundWith the aging of the Chinese population, the prevalence of depression and chronic diseases is continually growing among middle-aged and older adult people. This study aimed to investigate the association between chronic diseases and depression in this population.MethodsData from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2018 longitudinal survey, a 7-years follow-up of 7,163 participants over 45 years old, with no depression at baseline (2011). The chronic disease status in our study was based on the self-report of the participants, and depression was defined by the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). The relationship between baseline chronic disease and depression was assessed by the Kaplan–Meier method and Cox proportional hazards regression models.ResultsAfter 7-years follow-up, 41.2% (2,951/7163, 95% CI:40.1, 42.3%) of the participants reported depression. The analysis showed that participants with chronic diseases at baseline had a higher risk of depression and that such risk increased significantly with the number of chronic diseases suffered (1 chronic disease: HR = 1.197; 2 chronic diseases: HR = 1.310; 3 and more chronic diseases: HR = 1.397). Diabetes or high blood sugar (HR = 1.185), kidney disease (HR = 1.252), stomach or other digestive diseases (HR = 1.128), and arthritis or rheumatism (HR = 1.221) all significantly increased the risk of depression in middle-aged and older adult Chinese.ConclusionThe present study found that suffering from different degrees of chronic diseases increased the risk of depression in middle-aged and older adult people, and these findings may benefit preventing depression and improving the quality of mental health in this group

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication burden on resource-constrained workers and protect model privacy, split federated learning (SFL) has been released by integrating both data and model parallelism. Despite resource limitations, SFL still faces two other critical challenges in EC, i.e., statistical heterogeneity and system heterogeneity. To address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. Concretely, feature merging aims to merge the features from workers into a mixed feature sequence, which is approximately equivalent to the features derived from IID data and is employed to promote model accuracy. While batch size regulation aims to assign diverse and suitable batch sizes for heterogeneous workers to improve training efficiency. Moreover, MergeSFL explores to jointly optimize these two strategies upon their coupled relationship to better enhance the performance of SFL. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson edge devices, and the experimental results show that MergeSFL can improve the final model accuracy by 5.82% to 26.22%, with a speedup by about 1.74x to 4.14x, compared to the baselines

Fatigue crack propagation behavior of Ni-based superalloys after overloading at elevated temperatures

AbstractThe fatigue crack propagation behavior of three superalloys subjected to a single overloading at elevated temperatures was investigated. The fatigue crack propagation rate FCPR versus stress intensity factor range data da/dN—ΔK were calculated using the two-point secant method. It was found that the crack growth rates of the investigated materials were retarded after overloading with an overload ratio ROL=1.6. The size of the plastic zone in the front of the crack tip and its relation to loading level were discussed. The overload retardation effects are attributed to crack closure. The fatigue damage in the plastic zone can also be a factor to explain the overload retardation

Controlled Drug Delivery by Polylactide Stereocomplex Micelle for Cervical Cancer Chemotherapy

A stable doxorubicin (DOX)-loaded stereocomplex micelle drug delivery system was developed via the stereocomplex interaction between enantiomeric 4-armed poly(ethylene glycol)–poly(D-lactide) and poly(ethylene glycol)–poly(L-lactide) to realize control drug release and improve tumor cell uptake for efficient cervical carcinoma therapy. All these DOX-loaded micelles including poly(D-lactide)-based micelle (PDM/DOX), poly(L-lactide)-based micelle (PLM/DOX), and stereocomplex micelle (SCM/DOX) exhibited appropriate sizes of ∼100 nm for the enhanced permeability and retention (EPR) effect. In addition, compared to PDM/DOX and PLM/DOX, SCM/DOX exhibited the slowest DOX releaser, highest tumor cell uptake and the most efficient tumor cell suppression in vitro. Moreover, the excellent tumor inhibiting rates of the DOX-loaded micelles, especially SCM/DOX, were verified in the U14 cervical carcinoma mouse model. Increased tumorous apoptosis and necrosis areas were observed in the DOX-loaded micelles treatment groups, especially the SCM/DOX group. In addition, all these DOX-loaded micelles obviously alleviated the systemic toxicity of DOX. As a result, SCM can be a promising drug delivery system for the future therapy of cervical carcinoma

Thyroid function and epilepsy: a two-sample Mendelian randomization study

BackgroundThyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.MethodsGenome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran’s Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.ResultsThe findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.ConclusionThis study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study

Effects of principal stress rotation on the wave–seabed interactions

This paper simulates the wave–seabed interactions considering the principal stress rotation (PSR) by using the finite element method. The soil model is developed within the framework of kinematic hardening and the bounding surface concept, and it can properly consider the impact of PSR by treating the PSR generating stress rate independently. The simulation results are compared with centrifuge test results. The comparison indicates that the simulation with the soil model considering the PSR can better reproduce the test results on the development of pore water pressure and liquefaction than the soil model without considering the PSR. It indicates that it is important to consider the PSR impact in simulation of wave–seabed soil interactions

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Heterogeneity in gene expression and epigenetic states exists across individual cells. Here, the authors develop scCAT-seq, a technique for simultaneously performing ATAC-seq and RNA-seq within the same single cell

Two ultraviolet radiation datasets that cover China

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes