Abstract Federated learning is an effective solution for edge training, but the limited bandwidth and insufficient computing resources of edge devices restrict its deployment.Different from existing methods that only consider communication efficiency such as quantization and sparsification, this paper proposes an efficient federated training framew
Objective: To investigate the epidemiological pattern of animal bites in Yazd Province, central Iran.Methods: In this cross-sectional study, 8 545 individuals with animal bites who were referred to the Yazd Province Rabies Treatment Center were investigated using the Potty Training census method from April 2013 to March 2017.The variables included:
A critical issue in digital soil mapping (DSM) is the selection of data sampling method for model training.One emerging approach applies instance selection to reduce the size of the dataset by drawing only relevant samples in order to obtain a representative subset that is still large enough to preserve relevant information, but small enough to be
Energy is a fundamental force driving transformative processes across various domains.It is perpetual and adheres to the law of energy conservation, remaining indestructible.However, the challenge lies in the finite nature of conventional energy sources, which convert energy for intended purposes and cater to diverse needs, often escalating the ove
Inspired by the fact that the neural network, as the mainstream method for machine learning, has brought successes Aprons in many application areas, here we propose to use this approach for decoding hidden correlation among pseudo-random data and predicting events accordingly.With a simple neural network structure and a typical training procedure,