A New Implementation of Federated Learning for Privacy and Security Enhancement

Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parameter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm

Spatial Domain Management and Massive MIMO Coordination in 5G SDN

In 5G mobile communication systems, massive multiple-input multiple-output (MIMO) and heterogeneous networks (HetNets) play crucial roles to achieve expected coverage and capacity across venues. This paper correspondingly addresses software-defined network (SDN) as the central controller of radio resource management in massive MIMO HetNets. In particular, we identify the huge spatial domain information management and complicated MIMO coordination as the grand challenges in 5G systems. Our work accordingly distinguishes itself by considering more network MIMO aspects, including flexibility and complexity of spatial coordination. In our proposed scheme, SDN controller first collects the user channel state information in an effective way, and then calculates the null-space of victim users and applies linear precoding to that null-space. Simulation results show that our design is highly beneficial and easy to be deployed, due to its high quality of service performance but low computation complexity

Concurrent genotyping of Helicobacter pylori virulence genes and human cytokine SNP sites using whole genome amplified DNA derived from minute amounts of gastric biopsy specimen DNA

<p>Abstract</p> <p>Background</p> <p>Bacterial and cellular genotyping is becoming increasingly important in the diagnosis of infectious diseases. However, difficulties in obtaining sufficient amount of bacterial and cellular DNA extracted from the same human biopsy specimens is often a limiting factor. In this study, total DNA (host and bacterial DNA) was isolated from minute amounts of gastric biopsy specimens and amplified by means of whole genome amplification using the multiple displacement amplification (MDA) technique. Subsequently, MDA-DNA was used for concurrent <it>Helicobacter pylori </it>and human host cellular DNA genotyping analysis using PCR-based methods.</p> <p>Results</p> <p>Total DNA was isolated from gastric biopsy specimens of 12 subjects with gastritis and 16 control subjects having a normal mucosa. The DNA was amplified using a multiple displacement amplification (MDA) kit. Next, concurrent genotyping was performed using <it>H. pylori</it>-specific virulence gene PCR amplification assays, pyrosequencing of bacterial 16S rDNA and PCR characterisation of various host genes. This includes Interleukin 1-beta (<it>IL1B</it>) and Interferon-gamma receptor (<it>IFNGR1</it>) SNP analysis, and Interleukin-1 receptor antagonist (<it>IL1RN</it>) variable tandem repeats (VNTR) in intron 2. Finally, regions of the <it>vacA</it>-gene were PCR amplified using M13-sequence tagged primers which allowed for direct DNA sequencing, omitting cloning of PCR amplicons. <it>H. pylori </it>specific multiplex PCR assays revealed the presence of <it>H. pylori cagA </it>and <it>vacA </it>genotypic variations in 11 of 12 gastritis biopsy specimens. Using pyrosequencing, 16S rDNA variable V3 region signatures of <it>H. pylori </it>were found in 11 of 12 individuals with gastritis, but in none of the control subjects. Similarly, <it>IL1B </it>and <it>IFNGR1</it>-SNP and <it>IL1RN</it>-VNTR patterns could be established in all individuals. Furthermore, sequencing of M13-sequence tagged <it>vacA</it>-PCR amplicons revealed the presence of highly diverse <it>H. pylori vacA</it>-s/i/m regions.</p> <p>Conclusion</p> <p>The PCR-based molecular typing methods applied, using MDA-amplified DNA derived from small amounts of gastric biopsy specimens, enabled a rapid and concurrent molecular analysis of bacterial and host genes in the same biopsy specimen. The principles and technologies used in this study could also be applied to any situation in which human host and microbial genes of interest in microbial-host interactions would need to be sequenced.</p

CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning

Asynchronous federated learning aims to solve the straggler problem in heterogeneous environments, i.e., clients have small computational capacities that could cause aggregation delay. The principle of asynchronous federated learning is to allow the server to aggregate the model once it receives an update from any client rather than waiting for updates from multiple clients or waiting a specified amount of time in the synchronous mode. Due to the asynchronous setting, the stale model problem could occur, where the slow clients could utilize an outdated local model for their local data training. Consequently, when these locally trained models are uploaded to the server, they may impede the convergence of the global training. Therefore, effective model aggregation strategies play a significant role in updating the global model. Besides, client scheduling is also critical when heterogeneous clients with diversified computing capacities are participating in the federated learning process. This work first investigates the impact of the convergence of asynchronous federated learning mode when adopting the aggregation coefficient in synchronous mode. The effective aggregation solutions that can achieve the same convergence result as in the synchronous mode are then proposed, followed by an improved aggregation method with client scheduling. The simulation results in various scenarios demonstrate that the proposed algorithm converges with a similar level of accuracy as the classical synchronous federated learning algorithm but effectively accelerates the learning process, especially in its early stage

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer

Adiposity and asthma in adults: a bidirectional Mendelian randomization analysis of the HUNT Study

This article has been accepted for publication in Thorax, 2019 following peer review, and the Version of Record can be accessed online at http://dx.doi.org/10.1136/thoraxjnl-2019-213678. © Authors (or their employer(s))Background - We aimed to investigate the potential causal associations of adiposity with asthma overall, asthma by atopic status or by levels of symptom control in a large adult population and stratified by sex. We also investigated the potential for reverse causation between asthma and risk of adiposity. Methods - We performed a bidirectional one-sample Mendelian randomisation (MR) study using the Norwegian Nord-Trøndelag Health Study population including 56 105 adults. 73 and 47 genetic variants were included as instrumental variables for body mass index (BMI) and waist-to-hip ratio (WHR), respectively. Asthma was defined as ever asthma, doctor-diagnosed asthma and doctor-diagnosed active asthma, and was further classified by atopic status or levels of symptom control. Causal OR was calculated with the Wald method. Results - The ORs per 1 SD (4.1 kg/m2) increase in genetically determined BMI were ranged from 1.36 to 1.49 for the three asthma definitions and similar for women and men. The corresponding ORs for non-atopic asthma (range 1.42–1.72) appeared stronger than those for the atopic asthma (range 1.18–1.26), but they were similar for controlled versus partly controlled doctor-diagnosed active asthma (1.43 vs 1.44). There was no clear association between genetically predicted WHR and asthma risk or between genetically predicted asthma and the adiposity markers. Conclusions - Our MR study provided evidence of a causal association of BMI with asthma in adults, particularly with non-atopic asthma. There was no clear evidence of a causal link between WHR and asthma or of reverse causation