This paper introduces SFETEC, an innovative federated learning framework addressing the limitations of traditional methods like FedAvg. SFETEC splits training into base models and core models, reducing communication overhead, mitigating non-IID data issues, and enhancing training speed. Base models are trained at client devices, while core models are trained at edge servers and aggregated at the cloud. Preliminary results show that SFETEC significantly reduces communication overhead and training duration compared to the state-of-the-art baselines while enhancing privacy.

Reinforcement learning (RL) has been successfully applied in many fields for building autonomous systems, such as robotics and telecommunications. With its self-learning ability, RL provides a framework for learning from historical experience and adapting to dynamic environments. In response to the surge in network traffic and the evolving nature of traffic behavior, RL has emerged as a crucial technique for developing intelligent and adaptive traffic engineering (TE) solutions. However, most prior studies have focused on using a centralized unit (i.e., a single agent) to construct RL-based TE systems. While the centralized approach leverages global network information for solid performance, it encounters challenges related to scalability, dynamic network topology, and high monitoring overhead for collecting network information. This paper addresses these issues by introducing a jointly trained multi-agent reinforcement learning-based traffic engineering (MATE-JT) system, which operates as a distributed TE solution. Our approach utilizes multiple agents within a network node so that each agent can make independent routing decisions for a subset of flows. We take the approach of sharing parameters among agents and introduce a joint-training technique that facilitates simultaneous learning from multiple agents’ experiences. As a result, our proposed method enhances system performance while reducing training time. We evaluate the proposed approach using various network traffic datasets and demonstrate that MATE-JT improves the performance of TE (about 6.5%) and achieves faster convergence (about 35%) in large-scale networks when compared to state-of-the-art methods.

Federated Learning has emerged as a transformative paradigm in the realm of collaborative machine learning, enabling the training of global models across decentralized devices without the need for centralizing data. While Federated Learning has shown remarkable promise, a critical limitation lies in its ability to personalize models to individual clients. Current approaches predominantly emphasize improving the accuracy of trained clients, inadvertently sidelining the significance of accommodating unseen clients. Furthermore, most of the existing personalized federated learning approaches require new clients to provide labeled data and undergo extensive retraining, posing a substantial barrier and hindering the broader adoption and engagement of potential users within these systems.In this paper, we introduce a novel and comprehensive solution to address these challenges: a generalized method for Personalized Federated Learning. Our approach transcends the limitations of conventional Federated Learning techniques by not only optimizing the accuracy of trained clients but also ensuring exceptional performance among unseen clients, even in diverse settings. Throughout extensive experiments, our method demonstrates significant improvements concerning the performance of seen and unseen clients, respectively, while eliminating the need for labeled data and model re-training among unseen clients.

Traffic engineering (TE) is one of the most critical issues in networking, as it enables efficient and reliable network operations. With the advent of Machine Learning (ML) techniques, many ML-based TE methods have emerged in recent years, especially those employing Deep Neural Networks for future traffic prediction to enhance the performance of traditional approaches. However, current methods suffer from two major issues. Firstly, most prior works only solve the TE problem based on short-term traffic prediction, neglecting the network traffic dynamics over an extended time period. This oversight results in high network disturbance when numerous traffic flows need to be rerouted to adapt to traffic changes. Secondly, although traffic prediction models rely on historical traffic data to perform future prediction, ML-based TE studies often ignore the high overhead for network traffic monitoring. To address these issues, we propose a traffic prediction-based routing algorithm in which the routing rules can be applied to multiple time-steps without requiring changes, ultimately leading to reduced network disturbance. We employ the segment routing (SR) technique as the routing algorithm and formulate the multi-time-step segment routing method that incorporates future traffic prediction. To address the high monitoring overhead, we present an approach that combines partial traffic prediction and compressive sensing techniques to estimate unmeasured data. Through extensive experiments on real backbone network traffic datasets, we demonstrate that our proposal can achieve more than 80% of the optimal performance in reducing maximum link utilization while significantly reducing the number of routing changes and traffic monitoring cost.

This paper addresses the power control problem of a cell-free uplink massive Multiple-Input Multiple-Output (MIMO) system with mobile users, aiming at global sum-rate maximization under individual user Quality of Service (QoS) constraints. To solve this problem, we propose a Deep Deter-ministic Policy Gradient (DDPG)-based power control algorithm, whose design is tailored given the static and mobile user cases, respectively. In particular, different partial state space designs are investigated for each mobility use case, so as to achieve the best tradeoff between network performance and required learning complexity. Numerical results validate the effectiveness of the proposed method, which outperforms benchmark schemes both in terms of sum-rate and number of QoS satisfied users. It is shown that it can combine the advantages of traditional uniform max power control and max-min power control schemes. Furthermore, the proposed method is flexible and adapts itself well to dynamic and mobile environments.

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.

Mobile core networks have seen serious network resource contention among a huge amount of delay‐sensitive and delay‐insensitive traffic leading to network congestion or even failures. Delay‐insensitive traffic which is commonly generated by background applications or the operating systems to update the system's status can be postponed to yield network resources for delay‐sensitive traffic, especially in the peak times. This study proposes an efficient approach to delay‐insensitive traffic control leveraging software defined network (SDN) techniques. The study devises an effective data structure, namely flow tree , to resolve the flow aggregation, an essential issue in SDN‐based traffic control, especially when dealing with a huge number of small delay‐insensitive traffic flows. The proposed approach significantly reduces the inherent communication cost between the controller and Openflow switches and the storage cost in switches' expensive memories. This approach is significantly robust in controlling a huge number of small flows which are common in the modern mobile core networks and Internet of things environments. Experimental results from both simulated and real datasets reveal the effectiveness and efficiency of the proposed scheme.

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.