Global Round Research Articles

Global Rounds: Cardiovascular Care in Australia and New Zealand.

Global Rounds: Cardiovascular Care in Australia and New Zealand.

RETRACTED: SFL-MDrone: Synchronous federated learning enabled multi drones

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Federated Learning with Efficient Aggregation via Markov Decision Process in Edge Networks

Federated Learning (FL), as an emerging paradigm in distributed machine learning, has received extensive research attention. However, few works consider the impact of device mobility on the learning efficiency of FL. In fact, it is detrimental to the training result if heterogeneous clients undergo migration or are in an offline state during the global aggregation process. To address this issue, the Optimal Global Aggregation strategy (OGAs) is proposed. The OGAs first models the interaction between clients and servers of the FL as a Markov Decision Process (MDP) model, which jointly considers device mobility and data heterogeneity to determine local participants that are conducive to global aggregation. To obtain the optimal client participation strategy, an improved σ-value iteration method is utilized to solve the MDP, ensuring that the number of participating clients is maintained within an optimal interval in each global round. Furthermore, the Principal Component Analysis (PCA) is used to reduce the dimensionality of the original features to deal with the complex state space in the MDP. The experimental results demonstrate that, compared with other existing aggregation strategies, the OGAs has the faster convergence speed and the higher training accuracy, which significantly improves the learning efficiency of the FL.

Worker's Compensation Injury and Illness Care: A Global Round Table.

Work-related injuries and diseases have a significant impact on workers and their families, society, and the economy. There is a gap in the literature regarding the structures, content, quality, and outcomes of international occupational health systems serving injured and ill workers. This global round table was an attempt to elucidate, evaluate, and identify areas needing improvement. International occupational health professionals were identified via chain/snowball sampling and asked to answer five questions designed to evaluate the structures, processes, and outcomes of the workers' compensation systems in each country. Areas for improvement identified during this round table included timely access, reducing the impact of liability and eligibility determinations on access to medical care, equitable access to care, and the accuracy of reporting. Canada had successfully utilized a virtual approach to care for the geographically remote worker. International workers' compensation structures are designed to ensure timely access to quality care and services. Financial incentives optimize the safety of the working environment. There remain areas for improvement. Resources are limited, especially within the public health systems, which may delay care and affect quality. Informal and remote workers often do not have the same access to care. Occupational Health Services (OHS) and national reporting databases exist throughout the world but may not accurately capture data on informal, self-employed, small business, migrant, and remote workers.

Global Rounds: Poland.

Global Rounds: Poland.

Assessment of understory vegetation in a plantation forest of the southeastern United States using terrestrial laser scanning

Forests of the southeastern United States are home to large timber industries with substantial contributions to global round wood production and paper products. Despite the success of plantations and the large timber industries in this area, pine growth remains constrained due to the competition between planted pine and the species in the understory. Moreover, effective control of this interspecies competition had shown a significant two- to four-times increase in stand productivity. Thus, this study aims to evaluate the use of laser scanning derived data from Terrestrial Laser Scanner (TLS) to assess understory vegetation biomass, as conventional methods utilizing optical imagery have yet to be effective in quantifying and mapping evergreen understory in the southeastern coastal forests of the United States. For this study, we utilized TLS to scan the entire forest profile within 60 sample plots of an operational loblolly pine plantation in Nassau County, Florida, and collected understory biomass data through destructive sampling. We compared three TLS-based volume estimation methods for predicting understory biomass and applied the Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) regression method to derive an optimal model by integrating the most efficient volume estimation methods and other TLS-derived standard metrics. Our study identifies the 20th percentile of the echo height and a 3D volume metric based on mean height and understory cover as the most significant explanatory variables for the optimal model. The model exhibits high accuracy, with Adjusted R-squared (Adj. R2) of 0.80 and Root Mean Square Error (RMSE) of 234.8 g per square meter (g/m2). Additionally, the mean height and understory-based volume estimation method outperformed other methods, such as voxel count and three dimensional (3D) alpha hall-based method, with Adj. R2 of 0.79, 0.47, and 0.57, and RMSE values of 288, 448.6, and 413 g/m2, respectively, when used as a single variable in the model. The resulting model successfully predicted and quantified understory vegetation, showcasing TLS's potential to accurately capture biomass variation, particularly in evergreen-dominated pine plantation forests in the southeastern United States coastal regions. As a tool for monitoring understory, TLS can be used to aid plantation forest managers in identifying areas that require control measures for enhanced management practices.

Semi-Synchronous Personalized Federated Learning Over Mobile Edge Networks

Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). However, most existing PFL implementations rely on synchronous training to ensure good convergence performances, which may lead to a serious straggler problem, where the training time is heavily prolonged by the slowest UE. To address this issue, we propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS2), over mobile edge networks. By jointly optimizing the wireless bandwidth allocation and UE scheduling policy, it not only mitigates the straggler problem but also provides convergent training loss guarantees. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. On this basis, the bandwidth allocation problem can be solved using analytical solutions and the UE scheduling policy can be obtained by a greedy algorithm. Experimental results verify the effectiveness of PerFedS2 in saving the training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.

FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence

Federated learning (FL) is a promising training paradigm to achieve ubiquitous intelligence for future 6G communication systems. However, it is challenging to apply FL in 6G-enabled edge system since decentralized training consumes considerable energy and mobile devices are mostly battery-powered and resource-constrained. The intensive computation and communication cost of local updates accumulated by hundreds of global rounds bring about the energy bottleneck, which is exacerbated when the data is non identically and independently distributed (non-IID). To address this issue, we propose FedRelay, a generic multi-flow relay learning framework in which local updates are performed relay-by-relay in the training flow via model propagation. We also present a decentralized relay selection protocol that takes advantage of the diversity of cooperative communication networks. Following that, we investigate a FedRelay optimization problem to simultaneously minimize the energy consumption of local updates and alleviate the global non-IIDness. Technically, an approximation algorithm is proposed to jointly optimize computation frequency and transmission power, thus reducing the local training overhead. We further regulate the training topology of each flow by proposing a greedy relay policy that encourages effective information exchange among devices. Experiment results show that, compared to state-of-the-art federated learning algorithms, our learning framework can save up to 5 times the total energy required to achieve a reasonable global test accuracy.

Circulation Global Rounds: France

HomeCirculationVol. 147, No. 5Circulation Global Rounds: France No AccessArticle CommentaryRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessArticle CommentaryRequest AccessFull TextCirculation Global Rounds: France Nicolas Danchin, MD, PhD and Claire Bouleti, MD, PhD Nicolas DanchinNicolas Danchin Correspondence to: Nicolas Danchin, MD, PhD, Department of Cardiology, Hôpital Européen Georges Pompidou, 20 Rue Leblanc, 75015 Paris, France. Email E-mail Address: [email protected] https://orcid.org/0000-0001-9263-5051 Université de Paris Cité, Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris, France (N.D.). Search for more papers by this author and Claire BouletiClaire Bouleti https://orcid.org/0000-0001-6922-0093 Hôpital Paris St Joseph, France (N.D.). University of Poitiers, Clinical Investigation Center (INSERM 1204), Cardiology Department, Poitiers Hospital, France (C.B.). Search for more papers by this author Originally published30 Jan 2023https://doi.org/10.1161/CIRCULATIONAHA.122.059479Circulation. 2023;147:361–363FootnotesFor Sources of Funding and Disclosures, see page 363.Circulation is available at www.ahajournals.org/journal/circCorrespondence to: Nicolas Danchin, MD, PhD, Department of Cardiology, Hôpital Européen Georges Pompidou, 20 Rue Leblanc, 75015 Paris, France. Email nicolas.danchin.[email protected]com

FedFog: Network-Aware Optimization of Federated Learning Over Wireless Fog-Cloud Systems

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {FedFog}$ </tex-math></inline-formula> ) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {FedFog}$ </tex-math></inline-formula> in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {FedFog}$ </tex-math></inline-formula> . We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.

Effects of round-to-nearest and stochastic rounding in the numerical solution of the heat equation in low precision

Abstract Motivated by the advent of machine learning, the last few years have seen the return of hardware-supported low-precision computing. Computations with fewer digits are faster and more memory and energy efficient but can be extremely susceptible to rounding errors. As shown by recent studies into reduced-precision climate simulations, an application that can largely benefit from the advantages of low-precision computing is the numerical solution of partial differential equations (PDEs). However, a careful implementation and rounding error analysis are required to ensure that sensible results can still be obtained. In this paper we study the accumulation of rounding errors in the solution of the heat equation, a proxy for parabolic PDEs, via Runge–Kutta finite difference methods using round-to-nearest (RtN) and stochastic rounding (SR). We demonstrate how to implement the scheme to reduce rounding errors and we derive a priori estimates for local and global rounding errors. Let $u$ be the unit roundoff. While the worst-case local errors are $\mathcal{O}(u)$ with respect to the discretization parameters (mesh size and timestep), the RtN and SR error behaviour is substantially different. In fact, the RtN solution always stagnates for small enough $\varDelta t$, and until stagnation the global error grows like $\mathcal{O}(u\varDelta t^{-1})$. In contrast, we show that the leading-order errors introduced by SR are zero-mean, independent in space and mean-independent in time, making SR resilient to stagnation and rounding error accumulation. In fact, we prove that for SR the global rounding errors are only $\mathcal{O}(u\varDelta t^{-1/4})$ in one dimension and are essentially bounded (up to logarithmic factors) in higher dimensions.

Federated Learning for Intelligent Resources Allocation in Internet of Things

By using federated learning (FL), multiple Internet-of-Things (IoT) devices can construct a shared learning model without sending raw data to a centralized server. While FL has come a long way, it still has a ways to go. Issues such as heterogeneous user equipment (UEs) and data that is not independently and uniformly distributed are still obstacles. Facilitating a numerous UEs to participate in the learning in each cycle poses a possible problem of the huge communication budget. A weighted adjoining factor is presented to the localized gradient descent, generalizing the present FedAvg to solve these concerns. At the start of each global round, the proposed FL method randomly selects a fraction of the UEs to perform stochastic gradient descent in parallel. Then, we utilize the suggested FL method in cellular IoT to reduce either total power usage or execution duration of FL, in which a straightforward but effective path-following method is constructed for its explanations. At last, obtained simulations on poorly balanced data are presented to show that the presented FL algorithm is superior to FedAvg in terms of performance with respect to fast convergence. Moreover, they show that the suggested algorithm needs significantly less time and energy to train than the FL algorithm does when users contribute heavily to the learning process. These findings provide strong support for the suggested FL algorithm as a potential paradigm change for training mobile IoT networks with limited bandwidth.

Synchronizing asynchronous mobile robots with limited visibility using lights

There are three models of automated robots based on their activation schedules and time notions. They are FSYNC, SSYNC and ASYNC Model. The FSYNC-fully synchronous robots have the common notion of time and follow a global round of time. In every turn, all the robots are active and perform their job in synchronization. All the robots have the same notion of time in the SSYNC-semi-synchronous model, whereas in this model, all robots may not be active in each round. The activation time of ASYNC-asynchronous is independent and unpredictable. Research is ongoing to vanish the difference between asynchrony, semi-synchrony and synchrony by equipping the robots with various combination of lights and snapshots. Most of the research has been done on robots with unlimited visibility capabilities. In these works, a robot can view the colors of all other robots and based on that the decision is taken to change its own color thus achieves synchronization. Decision taking becomes challenging if the robots have limited view of the other robots. This paper proposed a technique of synchronizing the robots which are asynchronous by nature, using observable colored lights, where the robots have limited range of visibility.

Pumped heat energy storage with liquid media: Thermodynamic assessment by a Brayton-like model

A thermodynamic model for a steady state pumped heat energy storage in liquid media is presented: it comprises a coupled Brayton-like heat pump and heat engine cycles connected to a cryogenic liquid and a hot molten salt by counter-flow heat exchangers. The model considers non-isothermal heat transfers between the working fluid and the liquid media and explicitly includes a set of parameters accounting for the main internal and external losses, heat leak, and pinch point effects for both the heat pump (charge) and heat engine (discharge) modes. Specific expressions for the main magnitudes in the charge (as the input power and coefficient of performance) and discharge (as power output and efficiency) modes and the global round trip efficiency have been analytically derived in terms of isentropic efficiencies of the compressor and turbine, pressure losses in the heat exchange processes, effectivenesses of the external counter-flow heat exchangers, and coupling between the working fluid and the storage and cryogenic liquid media. Round trip efficiencies around of 35−40% have been obtained, internal losses being those with main negative influence on the calculated values. The strong constraints imposed by the pinch point effects and liquid media have been analyzed. The model provides a thermodynamic assessment of the main involved processes and their interplay for a selected arrangement (molten salts, cryogenic fluid, and the charge and discharge power blocks) in order to check parametric strategies for thermodynamic optimization and design. These strategies are based on a reduced set of parameters of the overall installation and without the high computational costs of dynamical models.

Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.

In silico molecular docking studies and ADME/T analysis of Some Flavonoids against the target of Mpro inCOVID-19

COVID-19 may be a devastating global pandemic round the world. While the bulk of infected cases appear mild, in some cases individuals present respiratory complications with possible serious lung damage. This virus can infect both animals and other people and its control are very difficult because there's no effective vaccine or drugs available in markets for the treatments for COVID-19 so far. During this study we docked the flavonoids against the target protein Mpro(6lu7) shows the binding energy between -8.68 kcal/mole to -6. 68 kcal/mol compare to native ligand (PRD_002214). The compounds Catechins, Luteoforol, Sappanchal-cone, Baicalein, Vitexin, Chrysosplenol, 5,6,7-Trimeth-oxyflavone showed almost equal binding energy towards the native ligand except Genistein. Our analyses revealed that the highest nine hits might function potential anti- SARS-CoV-2 lead molecules for further innovation and drug development process to combat COVID-19 also nearly as good drug likeness properties studied supported the Lipinski’s rules of 5.

Global Synchronization and Consensus Using Beeps in a Fault-Prone Multiple Access Channel

Global synchronization is an important prerequisite to many distributed tasks. Communication between processors proceeds in synchronous rounds. Processors are woken up in possibly different rounds. The clock of each processor starts in its wakeup round showing local round 0, and ticks once per round, incrementing the value of the local clock by one. The global round 0, unknown to processors, is the wakeup round of the earliest processor. Global synchronization (or establishing a global clock) means that each processor chooses a local clock round such that their chosen rounds all correspond to the same global round t.We study the task of global synchronization in a Multiple Access Channel (MAC) prone to faults, under a very weak communication model called the beeping model. Some processors wake up spontaneously, in possibly different rounds decided by an adversary. In each round, an awake processor can either listen, i.e., stay silent, or beep, i.e., emit a signal. In each round, a fault can occur in the channel independently with constant probability 0<p<1. In a fault-free round, an awake processor hears a beep if it listens in this round and if one or more other processors beep in this round. A processor still dormant in a fault-free round in which some other processor beeps is woken up by this beep and hears it. In a faulty round nothing is heard, regardless of the behaviour of the processors. An algorithm working with error probability at most ϵ, for a given ϵ>0, is called ϵ-safe. Our main result is the design and analysis, for any ϵ>0, of a deterministic ϵ-safe global synchronization algorithm that works in O(logp2⁡ϵ) time in any fault-prone MAC using beeps.As an application, we solve the consensus problem in a fault-prone MAC using beeps. Processors have input values from some set V and they have to decide the same value from this set. If all processors have the same input value, then they must all decide this value. Using global synchronization, we give a deterministic ϵ-safe consensus algorithm that works in time O((log⁡w)(logp⁡ϵ)) in a fault-prone MAC, where w is the smallest input value of all participating processors. We show that this time cannot be significantly improved, even when the MAC is fault-free.

Global Rounds: Cardiovascular Health, Disease, and Care in Spain.

Global Rounds: Cardiovascular Health, Disease, and Care in Spain.

The Evolution and Reshaping of Globalization: A Perspective Based on the Development of Regional Trade Agreements

AbstractWith the weakening role of the World Trade Organization multilateral trading system, the globalization pattern is moving toward regional economic integration. As a result, the number of regional trade agreements (RTAs) has rapidly increased. New trends in international economics and trade, such as the withdrawal of the US from the Trans‐Pacific Partnership and the trade disputes between the US and China, have revealed the intention of the developed countries represented by the US to reshape the direction of globalization. This paper combines the relevant research conclusions and current stylized facts to examine the evolution and reshaping of globalization. We find that: (i) countries have different attitudes toward the recent round of globalization, which are related to changes in the patterns of income distribution within countries caused by the last round of globalization; and (ii) regional economic development is an effective way to reshape globalization. The self‐strengthening effect of the hub country in the trade network has promoted global RTA expansion.

Global Round Robin: Efficient Routing With Cut-Through Switching in Fat-Tree Data Center Networks

Fat tree is a scalable and widely deployed data center network topology. In this paper, a novel framework for designing per-packet load-balanced routing algorithms in fat tree called global round robin (GRR) is proposed. Routing in fat tree consists of uprouting and downrouting. In uprouting, a packet is sent to a switch that is a common ancestor (CA) of the source (server) and the destination. In downrouting, the packet is sent from the CA switch to the destination. Assume that time is slotted and each slot can accommodate one packet. With GRR, in each slot, a connection configuration is formed by establishing an uprouting path from each server to a spine switch port such that no paths will cross each other. Packets are sent from sources to respective spine switches with cut-through switching. The connection configuration is updated in a round robin fashion such that in every $m$ slots, where $m$ is the number of spine switches, each server is connected to each spine switch exactly once. Since a CA does not need to be a spine switch, an improved GRR (IGRR) is then proposed to allow the nearest CA to intercept packets for downrouting. We prove that both GRR and IGRR can guarantee 100% throughput under a wide class of traffic. An analytical model is also constructed for studying their delay performance under uniform traffic. Finally, simulation results show that IGRR provides the best delay-throughput performance among all the existing per-packet load-balanced routing algorithms.