Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.

While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integrated into a classical neural network for the problem of detecting pneumonia from chest radiographs. We substitute one layer of a classical convolutional neural network with a variational quantum circuit to create a hybrid neural network. We train both networks on an image dataset containing chest radiographs and benchmark their performance. To mitigate the influence of different sources of randomness in network training, we sample the results over multiple rounds. We show that the hybrid network outperforms the classical network on different performance measures, and that these improvements are statistically significant. Our work serves as an experimental demonstration of the potential of quantum computing to significantly improve neural network performance for real-world, non-trivial problems relevant to society and industry.

Mazucheli et al. (2019) introduced the unit-Gompertz (UG) distribution and studied some of its properties. More specifically, they considered the random variable X =exp(-Y), where Y has the Gompertz distribution. In this paper, we consider the lower k-record values from this distribution. We obtain exact explicit expressions as well as several recurrence relations for the single and product moments of lower k-record values and then we use these results to compute the means, variances and the covariances of the lower k-record values. We make use of these calculated moments to find the best linear unbiased estimators (BLUEs) of the location and scale parameters of the UG distribution. Applying the relation between the BLUE and the best linear invariant estimator (BLIE), we obtain the BLIEs of the location and scale parameters, as well. In addition, based on the observed k-records, we investigate how to obtain the best linear unbiased predictor (BLUP) and best linear invariant predictor (BLIP) for a future k-record value. Confidence intervals for the unknown parameters and prediction intervals for future k-records are also discussed. A simulation study is performed to assess the point and interval estimators and predictors proposed in the paper. The results show that the BLIE and BLIP outperform the BLUE and BLIP, in the sense of mean squared error criterion, respectively. Finally, a real data set pertaining to COVID-19 2-records is analyzed.