A Comprehensive Survey On Client Selections in Federated Learning

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critical factor for the performance of the overall system. In this survey, we provide a comprehensive overview of the state-of-the-art client selection techniques in FL, including their strengths and limitations, as well as the challenges and open issues that need to be addressed. We cover conventional selection techniques such as random selection where all or partial random of clients is used for the trained. We also cover performance-aware selections and as well as resource-aware selections for resource-constrained networks and heterogeneous networks. We also discuss the usage of client selection in model security enhancement. Lastly, we discuss open issues and challenges related to clients selection in dynamic constrained, and heterogeneous networks

Besoins psychosociaux des mères d’enfants atteints de cancer durant la trajectoire de la maladie : une revue intégrative des écrits

Les besoins psychosociaux des parents d’enfants atteints de cancer est un concept qui a été étudié dans les années antérieures. Toutefois, malgré la pertinence des résultats de ces écrits scientifiques, il subsiste un manque de consensus en ce qui a trait aux besoins de ces parents. Notons que les conséquences négatives de la parentalité d'un enfant diagnostiqué avec un cancer sont généralement plus prononcées chez les mères, ce qui fait en sorte que le but de cette revue intégrative vise à établir l’état des connaissances entourant leurs besoins psychosociaux. Le Modèle humaniste des soins infirmiers – UdeM (Cara et al., 2016) a été choisi comme cadre conceptuel, afin de permettre une vision holistique centrée sur la personne. La méthode proposée par Whittemore et Knafl (2005) pour la conduite des revues intégratives a été utilisée en respectant les cinq étape suivantes: 1) l’identification du problème; 2) la recension des écrits; 3) l’évaluation des données; 4) l’analyse des données; et 5) la présentation des résultats. Vingt-et-un articles ont été retenus, la majorité (14/21) étant de bonne/très bonne qualité. Les résultats des écrits scientifiques retenus proposent deux grandes catégories de besoins psychosociaux chez les mères d’enfants diagnostiqués avec un cancer : les besoins liés au soi en tant que mère (qui sont d’ordre émotionnel, social, spirituel, ainsi que des besoins de sécurité) et les besoins liés à l’état de santé de l’enfant (qui englobent le besoin d’être présente au chevet de son enfant et le besoin d’être rassurées quant à son état de santé). Les résultats dressent un portrait pertinent et approfondi du phénomène. Ils mettent en lumière la plus-value de l’infirmière dans l’accompagnement des mères d’enfants atteints de cancer, et ce, durant toute la trajectoire de la maladie. Cet accompagnement implique pour l’infirmière de prendre en compte la culture de la mère (y compris la dimension sociale et spirituelle), quel que soit la phase de la maladie de son enfant. Ainsi, il est suggéré que l’infirmière,The psychosocial needs of parents of children with cancer is a concept that has been studied in previous years. However, despite the relevance of the results of this scientific literature, there remains a lack of consensus regarding the needs of these parents. Noting that the negative consequences of parenting a child diagnosed with cancer are generally more pronounced for mothers, this integrative review aims to establish the state of knowledge surrounding their psychosocial needs. The Humanistic Model of Nursing – UdeM (Cara et al., 2016) was chosen as the conceptual framework to allow for a holistic person-centered view. The method proposed by Whittemore et Knafl (2005) for conducting integrative reviews was used, following its five steps: 1) problem identification; 2) literature review; 3) data assessment; 4) data analysis; and 5) presentation of results. Twenty-one articles were selected, the majority (13/21) being of good/very good quality. The results of the selected scientific literature suggest two broad categories of psychosocial needs in mothers of children diagnosed with cancer: the needs related to the maternal self (which correspond emotional, social, spiritual, and safety needs); and the needs related to the child's health status (which include the need to be present at the bedside and the need for reassurance about the child's health status). The results provide a relevant and in-depth portrait of the phenomenon. They highlight the added value of nurses in accompanying mothers of children with cancer, throughout the pathway of the disease. This support implies that the nurse must consider the mother's culture (including the social and spiritual dimension), whatever the phase of her child's illness. Thus, it is suggested that the nurse, according to her humanistic values, be vigilant to the mother's freedom of choice and maintain a holistic posture, adapted to the situation

Hybrid Full-Duplex and Alternate Multiple Relay Selection and Beamforming in AF Cooperative Networks

In this paper, multiple relay selection and beamforming techniques are applied to a dual-hop full-duplex (FD) amplify-and-forward relaying network. We show that our proposed techniques allow the selection to be adaptive to the residual self-interference (SI) level for each of the available relays in the network. The adaptivity of our selection schemes is manifested through a hybrid system that is based on FD relaying and switching based on the overall channel conditions and the statistics of the residual SI channel for each of the relays. In particular, different proposed techniques are shown to be able to adaptively decide on when and how often the used relays should be switched in the case of overwhelming residual SI. Our results show that allowing such a fusion considerably improves the overall performance of the considered relaying scheme in terms of bit error rate compared with state-of-the-art relay selection schemes.This work was supported by the Qatar National Research Fund (a member of the Qatar Foundation) through GSRA under Grant #2-1-0601-14011.Scopu

Sparsity-aware multiple relay selection in large multi-hop decode-and-forward relay networks

In this paper, we propose and investigate two novel techniques to perform multiple relay selection in large multi-hop decode-and-forward relay networks. The two proposed techniques exploit sparse signal recovery theory to select multiple relays using the orthogonal matching pursuit algorithm and outperform state-of-the-art techniques in terms of outage probability and computation complexity. To reduce the amount of collected channel state information (CSI), we propose a limited-feedback scheme where only a limited number of relays feedback their CSI. Furthermore, a detailed performance-complexity tradeoff investigation is conducted for the different studied techniques and verified by Monte Carlo simulations.NPRP grant 6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation)Scopu

On Dependability Traffic Load and Energy Consumption Tradeoff in Data Center Networks

Mega data centers (DCs) are considered as efficient and promising infrastructures for supporting numerous cloud computing services such as online office, online social networking, Web search and IT infrastructure out-sourcing. The scalability of these services is influenced by the performance and dependability characteristics of the DCs. Consequently, the DC networks are constructed with a large number of network devices and links in order to achieve high performance and reliability. As a result, these requirements increase the energy consumption in DCs. In fact, in 2010, the total energy consumed by DCs was estimated to be about 120 billion Kilowatts of electricity in 2012, which is about 2.8% of the total electricity bill in the USA. According to industry estimates, the USA data center market achieved almost US 39 billion in 2009, growing from US 16.2 billion in 2005. One of the primary reasons behind this issue is that all the links and devices are always powered on regardless of the traffic status. The statistics showed that the traffic drastically alternates, especially between mornings and nights, and also between working days and weekends. Thus, the network utilization depends on the actual period, and generally, the peak capacity of the network is reached only in rush times. This non-proportionality between traffic load and energy consumption is caused by the fact that -most of the time- only a subset of the network devices and links can be enough to forward the data packets to their destinations while the remaining idle nodes are just wasting energy. Such observations inspired us to propose a new approach that powers off the unused links by deactivating the end-ports of each one of them to save energy. The deactivation of ports is proposed in many researches. However, these solutions have high computational complexity, network delay and reduced network reliability. In this paper, we propose a new approach to reduce the power consumption in DC. By exploiting the correlation in time of the network traffic, the proposed approach uses the traffic matrix of the current network state, and manages the state of switch ports (on/off) at the beginning of each period, while making sure to keep the data center fully connected. During the rest of each time period, the network must be able to forward its traffic through the active ports. The decision to close or open depends on a predefined threshold value; the port is closed only if the sum of the traffic generated by its connected node is less than the threshold. We also investigate the minimum period of time during which a port should not change its status. This minimum period is necessary given that it takes time and energy to switch a port on and off. Also, one of the major challenges in this work is powering off the idle devices for more energy saving while guaranteeing the connectivity of each server. So, we propose a new traffic aware algorithm that presents a tradeoff between energy saving and reliability satisfaction. For instance, in HyperFlatNet, simulation results show that the proposed approach reduces the energy consumption by 1.8*104 WU (Watt per unit of time) for a correlated network with1000-server (38 % of energy saving). In addition, and thanks to the proposed traffic aware algorithm, the new approach shows a good performance even in case of high failure rate (up to 30%) which means when one third of the links failed, the connection failure rate is only 0.7%. Both theoretical analysis and simulation experiments are conducted to evaluate and verify the performance of the proposed approach compared to the state-of-the-art techniques.qscienc

Sparsity-aware Multiple Relay Selection In Large Decode-and-forward Relay Networks

Cooperative communication is a promising technology that has attracted significant attention recently thanks to its ability to achieve spatial diversity in wireless networks with only single-antenna nodes. The different nodes of a cooperative system can share their resources so that a virtual Multiple Input Multiple Output (MIMO) system is created which leads to spatial diversity gains. To exploit this diversity, a variety of cooperative protocols have been proposed in the literature under different design criteria and channel information availability assumptions. Among these protocols, two of the most-widely used are the amplify-and-forward (AF) and decode-and-forward (DF) protocols. However, in large-scale relay networks, the relay selection process becomes highly complex. In fact, in many applications such as device-to-device (D2D) communication networks and wireless sensor networks, a large number of cooperating nodes are used, which leads to a dramatic increase in the complexity of the relay selection process. To solve this problem, the sparsity of the relay selection vector has been exploited to reduce the multiple relay selection complexity for large AF cooperative networks while also improving the bit error rate performance. In this work, we extend the study from AF to large-scale decode-and-forward (DF) relay networks. Based on exploiting the sparsity of the relay selection vector, we propose and compare two different techniques (referred to as T1 and T2) that aim to improve the performance of multiple relay selection in large-scale decode-and-forward relay networks. In fact, when only few relays are selected from a large number of relays, the relay selection vector becomes sparse. Hence, utilizing recent advances in sparse signal recovery theory, we propose to use different signal recovery algorithms such as the Orthogonal Matching Pursuit (OMP) to solve the relay selection problem. Our theoretical and simulated results demonstrate that our two proposed sparsity-aware relay selection techniques are able to improve the outage performance and reduce the computation complexity at the same time compared with conventional exhaustive search (ES) technique. In fact, compared to ES technique, T1 reduces the selection complexity by O(K^2 N) (where N is the number of relays and K is the number of selected relays) while outperforming it in terms of outage probability irrespective of the relays' positions. Technique T2 provides higher outage probability compared to T1 but reduces the complexity making a compromise between complexity and outage performance. The best selection threshold for T2 is also theoretically calculated and validated by simulations which enabled T2 to also improve the outage probability compared with ES techniques.qscienc

Exploiting Sparsity in Amplify-and-Forward Broadband Multiple Relay Selection

Cooperative communication has attracted significant attention in the last decade due to its ability to increase the spatial diversity order with only single-antenna nodes. However, most of the techniques in the literature are not suitable for large cooperative networks such as device-to-device and wireless sensor networks that are composed of a massive number of active devices, which significantly increases the relay selection complexity. Therefore, to solve this problem and enhance the spatial and frequency diversity orders of large amplify and forward cooperative communication networks, in this paper, we develop three multiple relay selection and distributed beamforming techniques that exploit sparse signal recovery theory to process the subcarriers using the low complexity orthogonal matching pursuit algorithm (OMP). In particular, by separating all the subcarriers or some subcarrier groups from each other and by optimizing the selection and beamforming vector(s) using OMP algorithm, a higher level of frequency diversity can be achieved. This increased diversity order allows the proposed techniques to outperform existing techniques in terms of bit error rate at a lower computation complexity. A detailed performance-complexity tradeoff, as well as Monte Carlo simulations, are presented to quantify the performance and efficiency of the proposed techniques. 2013 IEEE.This publication was made possible by NPRP grant 8-627-2-260 and NPRP grant 6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

A serious gaming approach to managing interference in ad hoc femtocell wireless networks

The aim of this paper is to optimize femtocell performance by managing interference between femtocell devices and between a femtocell and a macrocell. It achieves this using a three-phase approach that involves deployment of femtocells and control of resulting connections through consideration and management of path loss, transmission power, signal strength and coverage area. Simulation experiments of the proposed three-phase approach at a local college that experiences a poor service from the macrocell predict significant improvements in femtocell performance in terms of managing both types of interference: co-tier and cross-tier, number of users who experience good service, coverage, and mitigating outage probability. The overall and individual complexity of each phase has also been considered. Our approach has been compared with some existing techniques chosen from the literature that has been reviewed and its predicted performance is significantly improved in comparison to these

Mechanical recycling of polylactide, upgrading trends and combination of valorization techniques

The upcoming introduction of polylactides in the fractions of polymer waste encourages technologists to ascertain its valorization at the best quality conditions. Mechanical recycling of PLA represents one of the most cost-effective methodologies, but the recycled materials are usually directed to downgraded applications, due to the inherent thermomechanical degradation affecting its mechanical, thermal and rheological performance. In this review, the current state of mechanical recycling of PLA is reported, with special emphasis on a multi-scale comparison among different studies. Additionally, the applications of physical and chemical upgrading strategies, as well as the chances to blend and/ or composite recycled PLA are considered. Moreover, the different valorization techniques that can be combined to optimize the value of PLA goods along its life cycle are discussed. Finally, a list of different opportunities to nurture the background of the mechanical recycling of PLA is proposed, in order to contribute to the correct waste management of PLA wastes

Atomistic Investigation of High Temperature Material Behavior

High temperature mechanical behavior of materials is of critical importance in a variety of contexts: next-generation reentry vehicles, hyper sonic flights, nuclear plants, engines, among many others. Creep is the dominant failure mechanism for materials used in such applications. Atomistic design of next-generation ultra-high-temperature ceramic composites as well as a thorough understanding of the various complex micro-mechanisms of creep damage using state-of-the art atomistic methods is the main goal of this dissertation. There are two challenges that need to be overcome to accomplish this endeavor. The first one is aptly amplified in a quote by Professor Nabarro (2002) “The creep rate in a land-based power station must be less than 10−11s ... The present state of knowledge reveals specific questions that call for experimental investigation. Theory will contribute, but atomic computation, with a time scale of 10−11s, will not handle processes that take 1011s”. The other is that for the materials of interest, the so-called ultrahigh-temperature ceramics (ZrB2 and HfB2), atomistic potentials are not available. No type of atomistic methodology (molecular dynamics, Monte Carlo) can proceed without this. Furthermore, since oxidation and various related chemical reactions play a key role in the damage of such materials at high temperatures, the atomistic potential must be able to account for reactions. First-principle calculations are indeed possible without an empirical force field but such computations present severe limitations of the size scales they can access and of course, the enormous difficulty of modeling finite temperatures. In short, in this dissertation, I will focus on two aspects that can potentially pave the way for modeling high temperature behavior of ceramics: development of ReaxFF potentials for ZrB2 / HfB2 using quantum chemistry tools and implementation of algorithms that allow access to time scales relevant to creep deformation and damage.Mechanical Engineering, Department o