No abstract

No abstract

Registered at ISRCTN with study number ISRCTN31434033.

No abstract

No abstract

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.

No abstract

Despite trial registration being an accepted best practice, RCTs published in anesthesiology journals have a high rate of inadequate registration. While mandating trial registration has increased the proportion of adequately registered trials over time, there is still an unacceptably high proportion of inadequately registered RCTs. Among adequately registered trials, there are high rates of discrepancies between registered and reported outcomes, suggesting a need to compare a published RCT with its trial registry entry to be able to fully assess the quality of the study. If clinicians base their decisions on evidence distorted by primary outcome switching, patient care could be negatively affected.