Client Resources Research Articles

Antimicrobial Prescription Practices and Stewardship in Washington State Small and Mixed Animal Veterinary Medicine.

Judicious antimicrobial use is essential for the continued treatment of infections in small and mixed animal veterinary medicine. To better support Washington (WA) State veterinarians in antimicrobial stewardship, we surveyed licensed small and mixed animal veterinarians and led group conversations regarding antimicrobial prescription practices. Survey questions included demographic information, factors influencing prescription practices and clinical cases. Responses were summarised and logistic regressions were performed to identify factors associated with antibiotic treatment choices. Group conversations, led by a licensed veterinarian, focused on resource gaps for veterinarians, management of clinical scenarios and interpretation of minimum inhibitory concentrations (MICs) and breakpoints. A systematic qualitative analysis of conversation transcripts identified key themes such as common barriers to stewardship. Among 53 responses to clinical scenarios, veterinarians selected the most appropriate treatment choice, according to a veterinary microbiologist, 62% of the time. Variability was observed in culture and susceptibility practices and antibiotic choices. Survey respondents reported an influence of the client ability to medicate (92%), considerations of resistance (91%), client finances (75%) and availability of antimicrobials (75%) on their prescription decisions. There were no significant associations between opinions about contributing factors to antimicrobial resistance (AMR) or guidelines used and treatment choices in clinical scenarios. Among 15 veterinarians interviewed in group conversations, a systematic qualitative analysis of conversation transcripts revealed key themes, including reliance on human medicine as a resource and a lack of support for veterinarians in interpreting MICs and breakpoints. The variability in veterinary antibiotic treatment decisions in this study suggests a need for further dissemination of standardised antimicrobial stewardship resources for veterinarians. Client-related challenges and the cost of culture and susceptibility are major barriers to stewardship. To address these barriers, it is necessary to provide standardised, easy-to-access guidance for veterinarians in interpreting MICs and breakpoints, as well as develop antimicrobial use resources for clients.

Client resources and the prediction of therapeutic change using an interview: An exploratory study

Objectives Although psychotherapy research suggests that clients’ resources are related to positive outcomes, there is a lack of clinical tools available to consider their integration into psychotherapy. In this exploratory research, we studied the feasibility of a semi-structured interview to identify resources reported by clients at the onset of therapy and the relationship between resources and therapy outcomes. Methods Data consisted of interviews with 30 clients from a clinical trial, in which elicitation of resources and their relationship with the outcomes were the main study objectives. Results This interview was content analyzed and both adaptative resources and maladaptive resources (dysfunctional coping strategies) were identified. The association between the adaptive resources and the evolution of outcomes throughout treatment was analyzed. Time (i.e. sessions) and resources were negatively correlated with psychological distress. Moreover, resources positively influenced the impact of time on distress. Conclusions Clinicians should not take at face value resources that are self-reported, as they may reflect the maladaptive functioning of the client. The finding that clients with higher resources at onset have better outcomes points to the need to study how resources may be elicited effectively during therapy, and if this improves psychotherapy outcomes.

AIoT based sentiment analysis via multi‐scale context perception to enhance linguistic teaching

AbstractWith the rapid development of the Internet of Things (IoT) and artificial intelligence technology, real‐time text sentiment analysis plays an important role in online education. Due to the limited resources of clients, existing deep networks cannot be directly deployed in the edge node. In addition, deep convolutional networks cannot fully utilize contextual information. In order to resolve these issues, this paper proposes a multi‐scale context‐aware text sentiment analysis system based on cloud computing, in which a bidirectional long short‐term memory network (BiLSTM) model is deployed in the cloud server. The BiLSTM model can fully explore the contextual feature information of the text stream in online education. The real‐time text data are collected through terminal nodes, such as pad or computer, to send the cloud server. The experiments utilize three public text datasets to simulate the input of terminal nodes. The results show that the proposed system shows better accuracy than previous models and can return the emotional status in time.

Florida Doulas’ Perspectives on Their Role in Reducing Maternal Morbidity and Health Disparities

BackgroundMaternal mortality rates continue to rise in the United States. Considerable racial disparities exist, as Black women face two to three times the risks of dying from pregnancy-related complications compared with white women. Doulas have been associated with improved maternal outcomes. This study aimed to 1) investigate Florida doulas’ perspectives and influence on severe maternal morbidity/mortality and related inequities, as well as 2) identify opportunities for actionable change. The social ecological model, which acknowledges how individual, interpersonal, institutional, community, and public policy factors intersect, informed our analysis. MethodsThis qualitative study included seven online in-depth interviews and seven focus groups with doulas (N = 31) in Florida. Interview guides investigated how doulas perceive their role in the context of a) maternal morbidity and b) health disparities. ResultsDoulas associated maternal morbidity and health disparities with Black pregnant people, identifying racism as a major contributor. Doulas identified their role as one that most often intersects with the individual and interpersonal levels of the social ecological model. Doulas report providing positive social surveillance and emotional support, contributing education and resources, and championing for advocacy in health care settings. Actionable steps recommended by doulas to further mitigate health disparities include the integration of implicit bias training into doula certification programs, increasing public health funding to bolster a doula workforce that can serve racial and ethnic communities, establishing doula–hospital partnerships to improve relational communication, providing tailored resources for clients featuring representative messaging, and doulas’ continued engagement in positive social surveillance of their clients. ConclusionsDoulas perceived their role as integral to mitigating maternal morbidity and health disparities, particularly in the context of supporting and advocating for birthing persons on all levels of the social ecological model. Equitable access to doulas for low-income and/or minoritized populations may be one key strategy to improve maternal health equity.

Acceptability and effectiveness study of therapist-assisted internet-delivered cognitive behaviour therapy for agriculture producers

Agriculture producers are less likely to seek or to receive mental health services compared to the general population. Additional research is needed to identify effective and accessible mental health interventions for this underserved population. This study used a mixed-methods approach and open trial design to examine the acceptability and effectiveness of therapist-assisted internet-delivered cognitive behaviour therapy (ICBT) supplemented with an additional agricultural resource for clients from agricultural backgrounds receiving ICBT in routine care. Clients (n = 34) participated in an online, five-lesson course that provided psychoeducation and strategies for dealing with symptoms of anxiety and depression, with weekly therapist assistance. Clients also received a tailored resource (developed with input from those with an agricultural background) providing culturally specific information and case stories pertinent to agricultural communities. Intent-to-treat analyses showed that the ICBT program was effective in reducing anxiety and depression symptoms among the agricultural population. Large within-group pre-to-post-treatment Cohen's effect sizes of d = 1.14, 95 % CI [0.41, 1.86] and d = 1.15, 95 % CI [0.42, 1.87] were found for depression and anxiety, respectively and comparable to the same program offered to the general population. Clients also experienced reductions in perceived stress and significant improvements in resiliency from pre- to post-treatment. Semi-structured interviews conducted at post-treatment with the agricultural clients (n = 31) on their experiences with ICBT identified four main themes: perceived strengths of ICBT and the tailored resource, suggestions to improve service delivery for agriculture producers, clients experienced internal and external challenges to participating in ICBT, and the positive impact of the course reached beyond the client. Very high satisfaction rates were found. These results provide support for the acceptability and effectiveness of ICBT with a tailored resource offered in routine care among agriculture producers.

Therapists' problematic experiences when working with obsessive-compulsive disorder: a qualitative investigation of schema modes, mode cycles, and strategies to return to healthy adult mode.

Obsessive-compulsive disorder (OCD) is one of the most challenging pathologies for therapists, being chronic and often characterized by frequent relapses. The therapeutic relationship plays a critical role in the outcome of therapy. The dynamic interaction between therapist and client schema modes determines the quality of the relationship. of the present qualitative research is to investigate and conceptualize the triggers for therapists when working with OCD clients, the therapists' schema modes that are activated, and the strategies they use to get back into the Healthy Adult mode. Using the in-depth interview technique, we interviewed 15 psychotherapists of various therapeutic orientations. After several demographic items, the therapists answered some introductory questions about their general perceptions of working with this pathology. They were then guided to go through a specific therapeutic situation in imagery that they identified as particularly difficult to manage in the therapeutic relationship. For data analysis, we used the interpretative phenomenological analysis (IPA) method and thematic analysis. For therapists working with clients with OCD, two main categories of triggers have been identified: Perceived resistance to change and Superiority. Two other categories, Client immobilized by conflict and Abusive, emerged from our analysis. Therapists' reactions to triggers were synthesized into mode processes that (1) were not acted on and (2) were displayed in relation with the client. For mode processes that therapists did not explicitly act on, there was triggering of the Vulnerable Child in relationship with a Demanding Parent, followed by various coping modes, depending on the category of trigger, and in the displayed mode, most therapists operated in the Healthy Adult mode. With respect to the process, strategies used by therapists to return to Healthy Adult mode that stood out were: focusing on the process, self-acceptance, self-compassion, and somatic grounding strategies, and focusing on the client's resources.

Building a Client Resource and Communication Platform for Community-Based Organizations to Address Health and Social Needs: Co-Design Study.

Connecting individuals to existing community resources is critical to addressing social needs and improving population health. While there is much ongoing informatics work embedding social needs screening and referrals into health care systems and their electronic health records, there has been less focus on the digital ecosystem and needs of community-based organizations (CBOs) providing or connecting individuals to these resources. We used human-centered design to develop a digital platform for CBOs, focused on identification of health and social resources and communication with their clients. Centered in the Develop phase of the design process, we conducted in-depth interviews in 2 phases with community-based organizational leadership and staff to create and iterate on the platform. We elicited and mapped participant feedback to theory-informed domains from the Technology Acceptance Model, such as Usefulness and Ease of Use, to build the final product and summarized all major design decisions as the platform development proceeded. Overall, we completed 22 interviews with 18 community-based organizational leadership and staff in 2 consecutive Develop phases. After coding of the interview transcripts, there were 4 major themes related to usability, relevance, and external factors impacting use. Specifically, CBOs expressed an interest in a customer relationship management software to manage their client interactions and communications, and they needed specific additional features to address the scope of their everyday work, namely (1) digital and SMS text messaging communication with clients and (2) easy ways to identify relevant community resources based on diverse client needs and various program eligibility criteria. Finally, clear implementation needs emerged, such as digital training and support for staff using new platforms. The final platform, titled "Mapping to Enhance the Vitality of Engaged Neighborhoods (MAVEN)," was completed in the Salesforce environment in 2022, and it included features and functions directly mapped to the design process. Engaging community organizations in user-centered design of a health and social resource platform was essential to tapping into their deep expertise in serving local communities and neighborhoods. Design methods informed by behavioral theory can be similarly employed in other informatics research. Moving forward, much more work will be necessary to support the implementation of platforms specific to CBOs' needs, especially given the resources, training, and customization needed in these settings.

A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations

Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-constrained clients. To solve the problem of large-scale computation, in this article, a secure and efficient framework is proposed to outsource large-scale matrix determinants and linear equations to a cloud. Specifically, the proposed framework contains two protocols, which solve large-scale matrix determinant and linear equations, respectively. In the outsourcing protocols of large-scale matrix determinants and linear equations, the task matrix is encrypted and sent to the cloud by the client. The encrypted task matrix is directly computed by using LU factorization in the cloud. The computed result is returned and verified by the cloud and the client, respectively. The computed result is decrypted if it passes the verification. Otherwise, it is returned to the cloud for recalculation. The framework can protect the input privacy and output privacy of the client. The framework also can guarantee the correctness of the result and reduce the local computational complexity. Furthermore, the experimental results show that the framework can save more than 70% of computing resources after outsourcing computing. Thus, this article provides a secure and efficient alternative for solving large-scale computational tasks.

Occupational therapists’ experiences of using a new internet-based intervention - a focus group study

Background Research is limited about how the introduction of new ways of delivering and conducting occupational therapy, in accordance with expected changes in health care, is experienced by occupational therapists (OTs). Aim To explore how OTs experienced use of a new internet-based intervention, ‘Strategies for Empowering activities in Everyday life’ (SEE), focusing on supporting client resources to manage an active everyday life after stroke. Material and methods A focus group study with periodical repeated discussion was designed. Four sessions during a period of 22 months were conducted with a total of four OTs. Result Overall, the results reflected that the OTs experienced that the use of SEE for persons with stroke was a valuable complement to existing rehabilitation. The process of introducing SEE included a multifaceted transition involving context, intervention process and delivery that renewed occupational therapy. Conclusion These results indicate how the use of new internet-based interventions such as SEE can influence and support renewal of occupational therapy that extends beyond the particular intervention. Continued research is needed to explore more aspects of SEE feasibility.

SOME FUNDAMENTALS OF THERAPEUTIC WORK WITH FAMILIESIN THE LIGHT OF THE ASSUMPTIONS OF SOLUTION-FOCUSED THERAPY

The author, adressing one of the most popular therapeutic methods nowadays: short-term solu-tion-focused therapy (TSR; Brief Solution Focused Therapy, BSFT), shares her experience of appli-cation of the key rules of BSFT in the work of the "Jest Czas" Children and Youth Support Centre in Płock in the scope of shaping dialogue skills in the family and therapist's communication with its members, particularly in the context of cooperation with the school. She emphasises that a distinc-tive feature of BSFT is that the therapist does not analyse the reported problem in the first place, does not focus on the diagnosis and the patient's past, does not want to be just a guidance-giving expert, but works mainly on the "client's resources" (predispositions, skills, meaningful feelings, proper behaviours) that have proven themselves to function in the client's life. She further outlines five rules of BSFT therapeutic work, completed and reinterpreted in the centre she leads. The author underlines that in complementing these rules, the Płock centre also uses a scenario devel-oped by Maurice Berger, a French specialist in a child and family psychology, creator of the model of "therapeutic interviews with the family" (entretiens thérapeutiques avec la famille) and emphasis-es harmonising the philosophical foundation of BSFT (constructivism, postmodernism, narra-tivism) with the stream of Christian personalism.

FedDCS: A distributed client selection framework for cross device federated learning

In cross-device Federated Learning (FL) the existing client selection approaches are centralised, making the assumption that the participatory clients are resource-full and restrict their type to mobile phones. These assumptions limit the applications of FL and make it difficult for the global server to monitor the client (s) resources; as a consequence of which a high client drop-out rate is observed. Thus, a distributed cross-device FL framework with the provision of resource & time-aware client selection is required. This paper proposes a distributed cross-device FL client selection framework (FedDCS) which employs a novel data-type, resource, and time-aware protocol for FL client selection. FedDCS is designed to offload the client selection and their resource monitoring processes to the local edge servers with the objective to minimize client(s) drop-outs, model convergence time, number of message exchanges, and time to achieve a certain level of accuracy. Extensive experiments on two benchmark data sets: MNIST and FashionMNIST, are performed on the PyTorch-based FL platform to evaluate the performance of FedDCS on such metrics. The results show 100% client retention in FedDCS during FL model training for both MNIST and FashionMNIST datasets using IID and Non-IID settings. For the MNIST dataset, FedDCS shows 82% and 36% improvement in the time taken to reach the desired testing accuracy, and 18% and 10% improvement in model convergence time, for IID and Non-IID setting respectively as compared to the best performing state-of-the-art, i.e., FedMCCS. For the FashionMNIST dataset, FedDCS shows 17% and 6% improvement in the time taken to reach the desired testing accuracy, and 4% and 1% improvement in model convergence time, for IID and Non-IID setting respectively as compared to the best performing state-of-the-art, i.e., FedMCCS. In addition, FedDCS also significantly reduces the number of message exchanges during FL.

Efficiency-Boosting Federated Learning in Wireless Networks: A Long-Term Perspective

Federated learning (FL) can train a global model from clients' local dataset, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning applications on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round. However there are few works comprehensively consider the optimization for latency, accuracy and energy consumption from a long-term perspective in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless networks where client selection and bandwidth allocation are two crucial factors for improving the FL accuracy as well as reducing latency and energy consumption. We formulate the optimization problem as a mixed-integer problem, which minimizes the cost function over finite rounds while satisfying energy budget constraints of each client from a long-term perspective. To address this optimization problem, we propose the Per-round Energy Drift Plus Cost (PEDPC) algorithm in an online manner, including two parts: client selection and bandwidth allocation, which can be addressed by Increasing Time-Maximum Client Selection (ITMCS) algorithm and Barrier Method, respectively. Finally, the performance of the PEDPC algorithm is verified by extensive simulations in terms of latency, accuracy and energy consumption in IID and NON-IID data distributions.

Culture of providing psychological assistance taking into account the spiritual heritage of the client (case analysis)

This article is an integral part of a series of articles in which the positive dynamics of psychological work based on Ingush culture and religious values is shown by the example of specific cases of child-parent relations. In the first article of the series "Multicultural approach in psychological counseling: ethno-religious aspect (case analysis)" [1] on the example of father-daughter relations, we described the content of the process of psychological counseling, performed taking into account the ethno-cultural and spiritual characteristics of the client. The second article "Counseling in the cultural and spiritual context of the client (case analysis)" [2] reveals the process of culturally oriented counseling on the example of the father-son relationship. The third article "Ethnic and religious identity as a client's resources in working with a psychologist" [3] describes the process of psychological counseling, taking into account cultural, religious and individual diversity, using the example of the relationship between a spouse and her missing husband. In the fourth article "Cultural and spiritual dimension in psychological counseling (case analysis)" [4], an analysis of a multicultural approach in psychological counseling, which takes into account the ethnic and religious specifics of the client, is given on the example of a specific case of the work of a psychologist sensitive to cultural and spiritual differences with the relationship of mother and daughter. The positive dynamics of psychological work based on the Ingush culture and values of Islam is shown. In the fifth article, an attempt is made to comprehend the experience of a ten-year-old girl saying goodbye to her mother, to understand what helps a person when their loved one leaves this world [5]. This article describes the culture of providing psychological assistance, taking into account the spiritual heritage of the client, using the example of a specific case. The results of the study showed that the multicultural competence of the therapist provides them not only with a high level of the client’s trust, but also a professional approach based on the use of ethno-religious resources in therapeutic work.

Consensus on Criteria for Selection of Sign Language Mobile Apps: A Delphi Study

In the rapidly evolving digital learning landscape, sign language mobile apps are vital in advancing sign language teaching. However, ensuring the quality of these apps remains a critical challenge. To address this gap, this study employs the fuzzy Delphi technique to establish a robust set of criteria for evaluating the quality of sign language mobile apps. By leveraging the collective wisdom and expertise of a panel of experts, the fuzzy Delphi technique facilitates a structured process for achieving consensus on the essential factors contributing to evaluating sign language mobile apps. Through rigorous rounds of iterative feedback and analysis, the study identifies a comprehensive list of reliable criteria encompassing various dimensions, including functionality, usability, accessibility, and pedagogical effectiveness. The criteria established through this method serve as a valuable resource for developers, educators, and clients in selecting and developing top-notch sign language mobile apps. Developers can use the criteria as a guide during the design and development stages, ensuring that their apps meet the highest quality and user experience standards. Educators can rely on the criteria as a checklist for evaluating and selecting appropriate apps that align with their teaching objectives and cater to the diverse learning needs of their students. Clients, such as educational institutions or individuals seeking sign language learning resources, can make informed decisions by referring to the established criteria, promoting the adoption of clear and impactful sign language mobile apps. This study emphasizes the significance of applying the fuzzy Delphi method in the context of sign language mobile app assessment. Involving experts from relevant fields ensures that the established criteria capture the multifaceted nature of compelling sign language learning experiences. Developing a comprehensive and reliable set of criteria contributes to improving existing apps and encourages innovation in creating new apps that better serve the needs of sign language learners. Overall, this research extends the knowledge base of sign language teaching in the digital age by providing a robust framework for assessing the quality of sign language mobile apps. The findings of this study empower stakeholders in the education and technology sectors to make informed decisions, fostering the advancement of sign language teaching and promoting inclusivity in digital learning environments.

LGB Counseling Psychologists' Coming Out and Self-Disclosure in Counseling: Working Experiences and Strategies with LGBTQ Clients

<p>本研究目的為探究同志諮商心理師在同志諮商中出櫃及自我揭露的經驗，以及同志諮商心理師如何評估是否運用出櫃及自我揭露作為諮商策略。本研究訪談蒐集三位同志諮商心理師之同志諮商經驗，以敘事研究典範與方法進行資料分析。研究結果綜整同志諮商心理師在晤談中根據同志案主的主訴與狀態評估決定在諮商中是否出櫃及自我揭露，其中讓同志諮商心理師決定出櫃的意圖包含建立安全與平等的諮商關係，以及回應同志案主受污名影響的狀態；而選擇自我揭露的意圖則包含：（1）提供正向楷模經驗，拓展可能性、增加希望感；（2）鬆動案主對同志身分的污名認知及感受；（3）同理、支持及賦能。無論是出櫃還是自我揭露，受訪者皆會評估諮商目標以及助人效益與風險，助人者在自我揭露前也會評估與整理自己的狀態，並且依案主的需求來選擇揭露的內容。研究結果最後並呈現概念統整框架理解同志諮商心理師如何從個人生命經驗挪移到諮商專業經驗中選擇是否使用出櫃與自我揭露策略的三種情形：有出櫃但無自我揭露、有出櫃也有自我揭露，以及沒有出櫃但有自我揭露等。本研究根據結果提 出討論，並對同志諮商心理師及同志諮商實務、諮商專業及專業訓練和未來研究提出建議。</p> <p> </p><p>While coming out is a specific action that reveals one’s sexual-minority-identity, self-disclosure is a counseling technique that counselors expose their personal experiences relevant and beneficial to clients’ ultimate counseling goals. Since LGB Counseling psychologists are both sexual minority and counseling professionals, coming out and self-disclosure may seem to look similar from the outlook. Thus, they may come together, independently, consequently, or be obfuscated as the same. The study aims to investigate LGB counseling psychologists’ experiences working with LGBTQ clients, focusing on their assessment and evaluation of whether or not and how to use coming out and self-disclosure as counseling strategies. It also explored how LGB counseling psychologists’ life experiences as sexual minorities, professional training and theoretical orientations has any influences on their works with LGB clients. Three LGB counseling psychologists, self-identified as one lesbian in same-sex marriage, one gay in same-sex marriage, and one pan/bisexual female in hetero-sex marriage, were interviewed for their counseling experiences with LGBTQ clients and the data was analyzed in narrative research methodology. Results indicated how LGB counseling psychologists decided to come out or not in sessions and their strategies if they choose to come out and/or to self-disclose in counseling based on their evaluation of LGBTQ clients’ presenting issues, identity development stage, and conditions. LGB counseling psychologists reported that their intentions for coming out are to build safe and egalitarian counseling relationships with LGB clients, and to respond to clients’ internalized stigma and/or stigmatized loneliness and isolation. LGB counseling psychologists can use their coming out as examples and resources for clients to explore and clarify their internalized homophobia. They indicated that they would choose self-disclosure if they want to (1) provide a positive role model for and increase hope; (2) to loosen up LGBTQ clients’ perceptions and feelings of stigmatized identity, and (3) to provide empathy, support and empowerment. Participants would evaluate counseling goals, therapeutic effects, and risks when considering coming out or self-disclosure, as well as reflect on their own experiences and intentionally choose what to disclose to best meet clients’ needs. Conceptual frameworks are presented to further understand how LGB counseling psychologists choose to either come out without self-disclosure, to come out and disclose personal experiences, or to self-disclose without coming out. The study also found that counseling psychologist participants’ LGB life experiences, professional training and work experiences intertwined with each other and shaped their perspectives and evaluation of coming out and self-disclosure in working with LGB clients. We recognized that all participants in this study were LGB counseling psychologists with more than 6 years of clinical experiences, as well as having certain level of support from family and professional community. Thus, the results may not be able to apply to those who were less supported by significant others and less established professionally. Based on the findings, the study suggests that future research can investigate how LGB counseling psychologists evaluate and use their coming out and self-disclosure in sessions with heterosexual clients. In addition, we recommend that both LGB and heterosexual counseling psychologists diligently examine how our personal identities and life experiences impact on our clinical judgement, the intersections of personal and professional experiences. Therefore, LGB identities and life experiences can be valuable resources to use, not obstacles or stigmatized secret to hide when counseling psychologists working with LGB clients. By continuing to receive multicultural counseling training and enhancing understanding of our own intersexual identities rooted in the local sociocultural context, we counseling psychologists can better serve clients with diverse identities and experiences.</p> <p> </p>

Privacy preserving federated learning for full heterogeneity

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning.

Artificial intelligence of things at the edge: Scalable and efficient distributed learning for massive scenarios

Federated Learning (FL) is a distributed optimization method in which multiple client nodes collaborate to train a machine learning model without sharing data with a central server. However, communication between numerous clients and the central aggregation server to share model parameters can cause several problems, including latency and network congestion. To address these issues, we propose a scalable communication infrastructure based on Information-Centric Networking built and tested on Apache Kafka®. The proposed architecture consists of a two-tier communication model. In the first layer, client updates are cached at the edge between clients and the server, while in the second layer, the server computes global model updates by aggregating the cached models. The data stored in the intermediate nodes at the edge enables reliable and effective data transmission and solves the problem of intermittent connectivity of mobile nodes. While many local model updates provided by clients can result in a more accurate global model in FL, they can also result in massive data traffic that negatively impacts congestion at the edge. For this reason, we couple a client selection procedure based on a congestion control mechanism at the edge for the given architecture of FL. The proposed algorithm selects a subset of clients based on their resources through a time-based backoff system to account for the time-averaged accuracy of FL while limiting the traffic load. Experiments show that our proposed architecture has an improvement of over 40% over the network-centric based FL architecture, i.e., Flower. The architecture also provides scalability and reliability in the case of mobile nodes. It also improves client resource utilization, avoids overflow, and ensures fairness in client selection. The experiments show that the proposed algorithm leads to the desired client selection patterns and is adaptable to changing network environments.

Adaptive Upgrade of Client Resources for Improving the Quality of Federated Learning Model

Conventional systems are usually constrained to store data in a centralized location. This restriction has either precluded sensitive data from being shared or put its privacy on the line. Alternatively, federated learning (FL) has emerged as a promising privacy-preserving paradigm for exchanging model parameters instead of private data of Internet of Things (IoT) devices known as clients. FL trains a global model by communicating local models generated by selected clients throughout many communication rounds until ensuring high learning performance. In these settings, the FL performance highly depends on selecting the best available clients. This process is strongly related to the quality of their models and their training data. Such selection-based schemes have not been explored yet, particularly regarding participating clients having high-quality data yet with limited resources. To address these challenges, we propose in this article FedAUR, a novel approach for an adaptive upgrade of clients resources in FL. We first introduce a method to measure how a locally generated model affects and improves the global model if selected for aggregation without revealing raw data. Next, based on the significance of each client parameters and the resources of their devices, we design a selection scheme that manages and distributes available resources on the server among the appropriate subset of clients. This client selection and resource allocation problem is thus formulated as an optimization problem, where the purpose is to discover and train in each round the maximum number of samples with the highest quality in order to target the desired performance. Moreover, we present a Kubernetes-based prototype that we implemented to evaluate the performance of the proposed approach.

The power of connection: Self-care strategies of social wellbeing

Health care professionals have long regarded social contacts as a means for information or resources for patients and clients; however, new research in the field of relational neuroscience demonstrates how social connectedness is vital to human health and wellbeing. Social isolation and loneliness are viewed as risk factors that outpace common behavioral risk factors such as smoking, poor diet, lack of exercise and obesity. Health care professionals can learn to facilitate discussions and practice self-care strategies to strengthen connectedness, particular for those at-risk for social isolation and loneliness. Human beings are not only hardwired for close relationships, but when these falter or are limited, humans lose an essential means for accessing self-regulation, gaining an inner sense of safety, tapping into creative problem solving, and experiencing wholeness and enriched lives.

PSYCHOLOGICAL FEATURES OF YOUNG PEOPLE'S STRESS RESISTANCE IN WAR CONDITION

This article examines the concepts of stress resistance, intolerance to uncertainty, anxiety and depressive manifestations. The available data on their interrelationships and the studies on the specifics of military youth stress resistance i were reviewed. High stress resistance becomes critical in conditions of war and military conflicts. It includes adaptability to stressful situations and protecting the psyche from destruction, as well as maintaining a normal physical condition. Our task was to investigate the peculiarities of Ukrainian youth's stress resistance in a current special period for the country. The necessity to do this arose because of the need for psychological support of the population during the war period. Military personnel of the State Border Guard Service of Ukraine, as well as civilian youth having different jobs took part in the survey. The performed empirical study showed peculiarities of correlations of stress resistance with intolerance of uncertainty, anxiety and depressive states. Based on the obtained data, a regression model was calculated and substantiated, which was quite successful and could be useful for predicting and developing stress resistance of young people. The article also presents the recommendations, developed taking into account the results, for practical psychologists on working with youth during the war. They stress the importance of maintaining a healthy lifestyle, nutrition, sleep patterns, balancing work and free time, planning quality rest and propose breathing techniques, tapping techniques, search for a client's resources and personal developmental trainings.