Federated learning has emerged as a popular technique for distributing machine (ML) model training across the wireless edge. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two timescale hybrid federated learning</i> ( <monospace xmlns:xlink="http://www.w3.org/1999/xlink">TT-HF</monospace> ), semi-decentralized architecture that combines conventional device-to-server communication paradigm with device-to-device (D2D)...

While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from communicated model parameters. Differential (DP) often employed address such issues. However, impact DP on FL in multi-tier networks -- where hierarchical aggregations couple noise injection decisions at different tiers, and trust models are heterogeneous across subnetworks not well understood. To fill this gap, we develop \underline{M}ulti-Tier...

Although the openflow-based software defined network (SDN) architecture can ease workload of control and management separate it from switch/routing operations, a computation-resource limited controller still be congested by heavy flows then experiences serious delay. To enhance scalability reduce computation delay on SDN networks under Quality Service (QoS) requirements, hierarchical edge-cloud (HECSDN) system design is proposed with three features. First, sharing computational resources in...

Federated learning has received significant attention as a potential solution for distributing machine (ML) model training through edge networks. This work addresses an important consideration of federated at the network edge: communication delays between nodes and aggregator. A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes standard averaging algorithm to incorporate weighting current local global each device during synchronization step. Through...

The spindle of a machine tool plays key role in machining because the wear might result inaccurate production and decreased productivity. To understand condition tool, vector-based convolutional fuzzy neural network (vector-CFNN) was developed this study to diagnose faults from signals. vector-CFNN mainly comprises feature extraction part classification part. phase encompasses use layers pooling layers, while is facilitated through deployment network. fusion layer an important by being...

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DFL</monospace> ) to improve efficiency machine (ML) model by accounting for communication delays between edge and cloud. Different from traditional learning, leverages multiple stochastic gradient descent iterations on local...

It is essential to enhance the speed and accuracy of localization process gain robustness instantaneous properties adapt from practical environment a confidence band. In this paper, we proposed new received signal strength indicator-based method construct realtime band, which was composed by multiple region sets in multivariate normal distribution, associated target's trajectory for location-based services. Based on concept weighted positioning circular algorithm, designed objective function...

Algorithm parallelization diversifies a complicated computing task into small parts, and thus it receives wide attention when is implemented to evolutionary algorithms (EA). This works considers recently developed EA called the Swarm Intelligence Based (SIB) method as benchmark compare performance of two types parallel approaches: CPU-based approach via OpenMP GPU-based CUDA. The experiments are conducted solve an optimization problem in search supersaturated designs SIB method. Unlike...

Federated learning (FL) has emerged as a popular technique for distributing machine across wireless edge devices. We examine FL under two salient properties of contemporary networks: device-server communication delays and device computation heterogeneity. Our proposed StoFedDelAv algorithm incorporates local-global model combiner into the synchronization step. theoretically characterize convergence behavior obtain optimal weights, which consider global delay expected local gradient error at...

One of the important applications in Wireless Sensor Networks (WSNs) is video surveillance that includes tasks data processing and transmission. Processing transmission image WSNs has attracted a lot attention recent years. This known as Visual (WVSNs). WVSNs are distributed intelligent systems for collecting or with unique performance, complexity, quality service challenges. consist large number battery-powered resource constrained camera nodes. End-to-end delay very Quality Service (QoS)...

While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from communicated model parameters. In this work, we formalize Differentially Private Hierarchical Federated Learning (DP-HFL), DP-enhanced FL methodology that seeks improve privacy-utility tradeoff inherent in FL. Building upon recent proposals for Differential Privacy (HDP), one key concepts DP-HFL adapting DP noise injection at different layers an established...

Physical layer security in wireless communication deals mainly with unauthorized users, eavesdroppers, and/or jammers. It is crucial for network due to its broadcast nature and the channel state information (CSI) easily acquired by receivers. Thus, eavesdroppers can obtain utilizing estimated CSI. In this paper, we propose a secure estimation method which includes two components, i.e., designs of pilot signals estimator time division duplex orthogonal frequency multiplexing systems....

Incorporating sensor nodes with data aggregation capability to transmit less flow in wireless networks could reduce the total energy consumption. However, penalty from retransmissions due collision jeopardize advantages aggregation. In this paper, for first time, we consider consumption tradeoffs between and retransmission network. By using CSMA-CA MAC protocol, function is well formulated. We propose a rigorous non-linear mathematical formulation, where objective minimize of transmission...

Federated learning has emerged as a popular technique for distributing model training across the network edge. Its architecture is conventionally star topology be-tween devices and central server. In this paper, we propose two timescale hybrid federated (TT-Hf),which migrates to more distributed via device-to-device (D2D) communications. TT-HF, local occurs at successive gradient iterations, synchronization process timescales: (i) macro-scale, where global aggregations are carried out...

In recent years, ceramic membranes have been increasingly appreciated because of their superior thermal stability, chemical mechanical strength, and modular approach compared to polymeric membranes. The properties help meet the requirements for solid-liquid separation under severe conditions. This study applied atmospheric plasma spraying (APS) technique deposit alumina powders onto a 316L stainless steel substrate further introduced vapor-induced pore-forming (VI-PF) APS produce porous...

The problem of identifying and removing bottlenecks in a multi-terminal oil &amp; gas pipeline network while achieving quality delivery targets is very real complex problem. most effective way to meet the above business objective develop terminal simulation model. This paper case study describing approach designing multi-nodal model with resolve critical inter-company storage for major refiner. Various system modeling techniques approaches are elaborated focus on practical application. A...

The problem of validating a complex simulation model represents pipeline terminal performance with verifiable accuracy is difficult requiring extensive testing and calibration. This paper discusses case study the verification validation model. approach to deciding validity presented as well process verifying including methodology thresholds for acceptance. Ultimately demonstrates ability commercial optimization software work collaboratively determine an optimal business solution.

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated (DFL) to improve efficiency machine (ML) model by accounting for communication delays between edge and cloud. Different from traditional learning, DFL leverages multiple stochastic gradient descent iterations on local datasets within each global aggregation period intermittently aggregates parameters through servers in...

Recent advance in wireless sensor network (WSN) applications such as the Internet of Things (IoT) have attracted a lot attention. Sensor nodes to monitor and cooperatively pass their data, temperature, sound, pressure, etc. through under constrained physical or environmental conditions. The Quality Service (QoS) is very sensitive delays. When resources are when number receivers increases rapidly, how can provide good QoS (measured end-to-end delay) becomes critical problem. In this paper;...