Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or communication process in complex network environments. A new network architecture and paradigm with computing-measurable, perceptible, distributable, dispatchable, and manageable capabilities, computing and network convergence (CNC) of 6G networks can effectively support federated learning training and improve its communication efficiency. By guiding the participating devices' training in federated learning based on business requirements, resource load, network conditions, and arithmetic power of devices, CNC can reach this goal. In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning. The experiments address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters. The results show that the method we proposed can (1) cope well with complex network situations (2) effectively balance the delay distribution of participating devices for local training (3) improve the communication efficiency during the transfer of model parameters (4) improve the resource utilization in the network.Comment: 13 pages, 11 figures, accepted by Frontiers of Information Technology & Electronic Engineerin

Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation

The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel muti-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.Comment: Accepted to IEEE Open Journal of the Communications Societ

Effect of Thiazolidinedione Amide on Insulin Resistance, Creactive Protein and Endothelial Function in Young Women with Polycystic Ovary Syndrome

Purpose: To investigate the effect of thiazolidinedione amide (TZDA) treatment on high-sensitivity Creactive protein (hsCRP) levels and endothelial dysfunction in patients with polycystic ovary syndrome (PCOS).Methods: Twenty five women (mean age 24.7 ± 3.9 years; mean body mass index (BMI), 23.2 ± 4.0 kg/m2) with PCOS were treated with 15 μM TZDA daily for 12 months. Serum levels of testosterone, leutenizing hormone (LH), follicle stimulating hormone (FSH), sex hormone-binding globulin (SHBG), insulin and hsCRP were measured. BMI, hirsutism scores and insulin sensitivity indices were also calculated prior to and after TZDA treatment. Brachial artery responses to stimuli was used to measure arterial endothelium and smooth muscle function prior to and after the treatment.Results: TZDA treatment caused a significant (p < 0.05) decrease in serum testosterone from 93.1 ± 40.3 to 54.8 ± 19.5 ng/dl and fasting insulin concentration from 11.9 ± 6.8 to 9.23 ± 5.13 U/mL. Insulin resistance index significantly (p < 0.05) improved and hirsutism score decreased significantly from 11.6 ± 2.0 to 6.8 ± 2.0. BMI, waist circumference, serum total cholesterol, low-density lipoprotein (LDL)- cholesterol, FSH and LH levels remained almost unchanged. Twenty-four of the women reverted to regular menstrual cycles. SHBG levels showed a significant (p < 0.05) increase from 24.8 ± 9.5 to 49.1 ± 13.5 nmol/L after TZDA treatment. Serum hsCRP levels decreased (p < 0.05) from 0.25 ± 0.1 to 0.09 ± 0.02 mg/dL while endothelium-dependent vascular responses significantly improved (p < 0.05) following TZDA treatment (9.9 ± 3.9 vs 16.4 ± 5.1%).Conclusion: TZDA treatment improves insulin sensitivity, decreases androgen production and improves endothelial dysfunction in young women with PCOS.Keywords: Thiazolidinedione amide, Insulin sensitivity, Endothelial dysfunction, Polycystic ovary syndrom