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Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets. IntroductionMobile phones, wearable devices, and smart homes are just a few of the modern distributed networks generating massive amounts of data each day. Due to the growing storage and computational power of devices in these networks, it is increasingly attractive to store data locally and push more network computation to the edge. The nascent field of federated learning explores training statistical models directly on devices [37]. Examples of potential applications include: learning sentiment, semantic location, or activities of mobile phone users; predicting health events like low blood sugar or heart attack risk from wearable devices; or detecting burglaries within smart homes [3,39,42]. Following [25,36,26], we summarize the unique challenges of federated learning below.1. Statistical Challenges: The aim in federated learning is to fit a model to data, {X 1 , . . . , X m }, generated by m distributed nodes. Each node, t ∈ [m], collects data in a non-IID manner across the network, with data on each node being generated by a distinct distribution X t ∼ P t . The number of data points on each node, n t , may also vary significantly, and there may be an underlying structure present that captures the relationship amongst nodes and their associated distributions. Systems Challenges:There are typically a large number of nodes, m, in the network, and communication is often a significant bottleneck. Additionally, the storage, computational, and communication capacities of each node may differ due to variability in hardware (CPU, memory), network connection (3G, 4G, WiFi), and power (battery level). These systems challenges, compounded with unbalanced data and statistical heterogeneity, make issues such as stragglers and fault tolerance significantly more prevalent than in typical data center environments.In this work, we propose a modeling approach that differs significantly from prior work on federated learning, where the aim thus far has been to train a single global model across the network [25,36,26]. Instead, we address statistical challenges in the federated setting by learning separate models for each node, {w 1 , . . . , w m }. This can be naturally captured through a multi-task learning (MTL) framework, where the goal is to consider fitting separate but related models simultaneously [14,2,57,28].Unfortunately, current multi-ta...

These results suggest that this walking protocol, when translated into Chinese and when accompanied by a weekly telephone reminder and other interactions with a Chinese-speaking nurse, is appropriate to use without additional cultural modification. Future research should examine other components of Chinese culture or should apply this protocol for a longer period of time.

Mental health nurses could help family caregivers become aware of the emotional pain that suicidal people experience and then promote their positive attitudes towards their suicidal relatives. Furthermore, family caregivers could increase their ability to care for their suicidal relatives, which could reduce the numbers of suicides.

Purpose: To evaluate the efficacy of logotherapy on meaning in life, depression, hopelessness, and suicide ideation in patients with depression.Design and Methods: A quasi-experimental approach was used and 86 participants were recruited from a psychiatric department in Taiwan. The experimental group received logotherapy for 12 weeks. The control group received depression education as usual. Findings:The results revealed significant differences between and within the groups for meaning in life, depression, hopelessness, and suicide ideation.Practice Implications: Logotherapy was an effective method for increasing meaning in life, reducing the degree of depression, hopelessness, and suicidal ideation for patients with depression.

The results of this study may enhance nurses' understanding of the psychological process of patients receiving initial chemotherapy for breast cancer, thereby helping nurses to provide appropriate assistance to improve the quality of patient care.

Background Globally, cancer is the second leading cause of death. Breast cancer and gynecological cancer can damage patients’ body image and lead to psychological distress, depression, and demoralization syndrome. No studies have explored the effect of logotherapy in gynecological cancer patients’ psychological distress, depression, and demoralization. Objective To evaluate the effects of logotherapy on distress, depression, and demoralization in breast cancer and gynecological cancer patients. Methods A quasi-experimental design was used in this study, involving 61 breast cancer and gynecological cancer patients: 31 in the experimental group and 30 in the control group. Participants in the experimental group received logotherapy 4 to 6 times during the 12 weeks of intervention. Outcomes were measured by the (1) Distress Thermometer, (2) Patient Health Questionnaire, and (3) Demoralization Scale Mandarin Version (DS-MV). Results Distress Thermometer did not differ between groups, but significant differences in favor of the intervention group were noted in the Patient Health Questionnaire (U = 674.500, P = .002); the DS-MV subcategories of loss of meaning (U = 706.500, P = .000), dysphoria (U = 673.000, P = .002), disheartenment (U = 670.000, P = .003), helplessness (U = 621.000, P = .022), and sense of failure (U = 629.500, P = .016); and the total score of the DS-MV (U = 728.500, P = .000). Conclusion Logotherapy was effective in the reduction of breast cancer and gynecological cancer patients’ depression and demoralization. Implications for Practice Clinical professionals could add logotherapy to the treatment for breast cancer and gynecological cancer patients to reduce their depression and demoralization.

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