With the exponential growth of the Internet of things (IoT) in remote sensing image applications, network resource orchestration and data privacy are significant aspects to handle in bigdata cellular networks. The image data sharing procedure toward central cloud servers in order to perform real-time classifications has leaked client personalization and heavily burdened the communication networks. Thus, the deployment of IoT image sensors in privacy-constrained sectors requires an optimized federated learning (FL) scheme to efficiently consider both aspects of securing data privacy and maximizing the model accuracy with sufficient communication and computation resources. In this article, an adaptive model communication scheme with virtual resource optimization for edge FL is proposed by converging a deep q-learning algorithm to enforce a self-learning agent interacting with network functions virtualization orchestrator and software-defined networking based architecture. The agent targets to optimize the resource control policy of virtual multi-access edge computing entities in virtualized infrastructure manager. The proposed scheme trains the learning model and weighs the optimal actions for particular network states by using an epsilon-greedy strategy. In the exploitation phase, the scheme considers multiple spatial-resolution sensing conditions and allocates computation offloading resources for global multiconvolutional neural networks model aggregation based on the congestion states. In the simulation results, the quality of service and global collaborative model performance metrics were evaluated in terms of delay, packet drop ratios, packet delivery ratios, loss values, and overall accuracy.

Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round communications of multi-dimensional model updates between local participants and the central parameter server. With an elastic edge-assisted aggregation procedure, the burden of backbone congestion can be handled for multi-service model communications. To provide an adaptive virtual resource orchestration, the integration of software-defined networking (SDN), network functions virtualization (NFV), EC, and double deep q-networks is emerged, which accurately gathers the state observation from local and edge nodes in data plane and NFV infrastructure (NFVI) for applying actions of allocated resource predetermination and recommended offloading policy. The reward formulation considers the minimization of completion time, energy consumption, and round communications between local participants and the selected NFV-enabled EC (NFVeEC) node until the global model is constructed with satisfying accuracy. In this paper, we proposed a decentralized SDN controller as an agent to enable a softwarization mechanism and interact with the virtualization-based EFL environment. The architecture adoption, agent controller, and orchestrator allow the centralized view for virtual network functions placement with efficient virtual machine mapping to perform edge update procedures and create the forwarding path effectively. The recommended NFVeEC for local-EFL updates is based on reward output and proposed agent valuation.

Federated learning (FL) is the up-to-date approach for privacy constraints Internet of Things (IoT) applications in next-generation mobile network (NGMN), 5 ^th generation (5G), and 6 ^th generation (6G), respectively. Due to 5G/6G is based on new radio (NR) technology, the multiple-input and multiple-output (MIMO) of radio services for heterogeneous IoT devices have been performed. The autonomous resource allocation and the intelligent quality of service class identity (IQCI) in mobile networks based on FL systems are obligated to meet the requirements of privacy constraints of IoT applications. In massive FL communications, the heterogeneous local devices propagate their local models and parameters over 5G/6G networks to the aggregation servers in edge cloud areas. Therefore, the assurance of network reliability is compulsory to facilitate end-to-end (E2E) reliability of FL communications and provide the satisfaction of model decisions. This paper proposed an intelligent lightweight scheme based on the reference software-defined networking (SDN) architecture to handle the massive FL communications between clients and aggregators to meet the mentioned perspectives. The handling method adjusts the model parameters and batches size of the individual client to reflect the apparent network conditions classified by the k-nearest neighbor (KNN) algorithm. The proposed system showed notable experimented metrics, including the E2E FL communication latency, throughput, system reliability, and model accuracy.

The growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network function virtualization (NFV)-enabled environment. In IoT networks, the configuration of adaptive SFC has emerged to ensure optimality and elasticity of resource expenditure. In this paper, priority-aware resource management for adaptive SFC is provided by modeling the configuration of real-time IoT service requests. The problem models of the primary features that impact the optimization of configuration times and resource utilization are studied. The proposed approaches query the promising embedded deep reinforcement learning engine in the management layer (e.g., orchestrator) to observe the state features of VNFs, apply the action on instantiating and modifying new/created VNFs, and evaluate the average transmission delays for end-to-end IoT services. In the embedded SFC procedures, the agent formulates the function approximator for scoring the existing chain performance metrics. The testbed simulation was conducted in SDN/NFV topologies and captured the average of rewards, delays, delivery ratio, and throughput as −48.6666, 10.9766 ms, 99.9221%, and 615.8441 Mbps, which outperformed other reference approaches, following parameter configuration in this environment.

An Intelligent Real-Time Traffic Control Based on Mobile Edge Computing for Individual Private Environment: The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5^th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensors data. The simulation results are validated through computer software simulation.