Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, we show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time

Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks

In this paper, the problem of minimizing energy and time consumption for task computation and transmission is studied in a mobile edge computing (MEC)-enabled balloon network. In the considered network, each user needs to process a computational task in each time instant, where high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational abilities to process the tasks offloaded from their associated users. Since the data size of each user's computational task varies over time, the HABs must dynamically adjust the user association, service sequence, and task partition scheme to meet the users' needs. This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables each HAB to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the prediction of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real data of city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 16.1% compared to a conventional centralized method

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.Comment: 14 pages, 13 figures, accepted by IEEE Transactions on Wireless Communication

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

In the past decade, the automotive light detection and ranging (LiDAR) has been experiencing a rapid expansion stage. Many researchers have been involved in the research of LiDARs and have installed it in vehicles as a means of enhancing autopilot capabilities. Compared with a traditional millimeter wave radar, LiDARs have many advantages such as the high imaging resolution, long measurement range, and the ability to reconstruct 3D information around the vehicle. These features make LiDARs one of the crucial research hotspots in the field of autopilot. The basic principles of LiDARs are the same as those of a laser rangefinder. The distance information can be obtained by locating the echo instant corresponding to the laser emission moment. But if the interval between two adjacent laser pulses is extremely narrow, the regions of the light emission and echo will be overlapped. Therefore, a range ambiguity will occur and the distance information calculation process will become abnormal. Besides, the high resolution of LiDARs is also characterized by its extremely high emissions frequency. Whilst the information about the surrounding environment of an automotive car can be retrieved more accurately, it means that the possibility of range ambiguity is also increasing at the same time. In this paper, we propose an algorithm for solving the range ambiguity problem of the LiDARs based on the concept of classification and can be accelerated by the FPGA approach, for the first time in the field of an automotive LiDAR. The algorithm can be performed by employing a single wavelength pulsed laser and can be specifically optimized for the demands of field programmable gate arrays (FPGAs). While guaranteeing the high resolution of LiDARs, the attenuation of the measurement ability should exceed due to the occurrence of range ambiguity. It can also match the demand for the processing speed of large amounts of point cloud information data. Through controlling the cost of the whole device, the performance of the LiDAR can be greatly improved. The result of this paper might provide a bright future of automotive LiDARs with the high data processing efficiency and the high resolution at the same time

The Acute Toxicity and Hematological Characterization of the Effects of Tentacle-Only Extract from the Jellyfish Cyanea capillata

To investigate the hematologic changes and the activities of jellyfish venoms other than hemolytic and cardiovascular toxicities, the acute toxicity of tentacle-only extract (TOE) from the jellyfish Cyanea capillata was observed in mice, and hematological indexes were examined in rats. The median lethal dose (LD50) of TOE was 4.25 mg/kg, and the acute toxicity involved both heart- and nervous system-related symptoms. Arterial blood gas indexes, including pH, PCO2, HCO3−, HCO3std, TCO2, BEecf and BE (B), decreased significantly. PO2 showed a slight increase, while SO2c (%) had no change at any time. Na+ and Ca2+ decreased, but K+ increased. Biochemical indexes, including LDH, CK, CK-MB, ALT, AST and sCr, significantly increased. Other biochemical indexes, including BUN and hemodiastase, remained normal. Lactic acid significantly increased, while glucose, Hct% and THbc showed slight temporary increases and then returned to normal. These results on the acute toxicity and hematological changes should improve our understanding of the in vivo pathophysiological effects of TOE from C. capillata and indicate that it may also have neurotoxicity, liver toxicity and muscular toxicity in addition to hemolytic and cardiovascular toxicities, but no kidney or pancreatic toxicity

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China:the PLANS (platelet lymphocyte age neutrophil sex) model

Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality

Artificial disc and vertebra system: a novel motion preservation device for cervical spinal disease after vertebral corpectomy

OBJECTIVE: To determine the range of motion and stability of the human cadaveric cervical spine after the implantation of a novel artificial disc and vertebra system by comparing an intact group and a fusion group. METHODS: Biomechanical tests were conducted on 18 human cadaveric cervical specimens. The range of motion and the stability index range of motion were measured to study the function and stability of the artificial disc and vertebra system of the intact group compared with the fusion group. RESULTS: In all cases, the artificial disc and vertebra system maintained intervertebral motion and reestablished vertebral height at the operative level. After its implantation, there was no significant difference in the range of motion (ROM) of C3-7 in all directions in the non-fusion group compared with the intact group (p>;0.05), but significant differences were detected in flexion, extension and axial rotation compared with the fusion group (