Reviewerly/exHarmony huggingface dataset:
A Profit-Aware Adaptive Approach for In-Network Traffic Classification

In-network traffic classification is a new paradigm in developing accurate and early-stage traffic classification solutions. However, despite having good accuracy, the one-fit machine learning model becomes outdated as the traffic pattern changes over time. This changing traffic pattern leads to misclassification, i.e., incorrect mapping of traffic flows to the Quality of Service (QoS) classes, resulting in a service quality violation and the imposition of a penalty. This paper proposes a profit-aware adaptive traffic classification approach in the data plane. We particularly design an economic model to measure the impact of per-class misclassification rate on the infrastructure provider's profit and use an adaptive method to handle misclassification directly inside a programmable data plane. The evaluation result shows that optimal path allocation for various traffic classes determines the targeted revenue, while improving classifier accuracy reduces penalty and maintains the maximum profit.
Regulating Clients' Noise Adding in Federated Learning Without Verification

In federated learning (FL), clients cooperatively train a global model without revealing their raw data but gradients or parameters, while the local information can still be disclosed from local outputs transmitted to the parameter server. With such privacy concerns, a client may overly add artificial noise to his local updates to compromise the global model training, and we prove the selfish noise adding leads to an infinite price of anarchy (PoA). This paper proposes a novel pricing mechanism to regulate privacy-sensitive clients without verifying their parameter updates, unlike existing privacy mechanisms that assume the server's full knowledge of added noise. Without knowing the ground truth, our mechanism reaches the social optimum to best balance the global training error and privacy loss, according to the difference between a client's updated parameter and all clients' average parameter. We also improve the FL convergence bound by refining the aggregation rule at the server to account for different clients' noise variances. Moreover, we extend our pricing scheme to fit incomplete information of clients' privacy sensitivities, ensuring their truthful type reporting and the system's ex-ante budget balance. Simulations show that our pricing scheme greatly improves the system performance especially when clients have diverse privacy sensitivities.
Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks

In this paper, we use a variance-based genetic ensemble (VGE) of Neural Networks (NNs) to detect anomalies in the satellite's historical data. We use an efficient ensemble of the predictions from multiple Recurrent Neural Networks (RNNs) by leveraging each model's uncertainty level (variance). For prediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the optimal structure for each RNN model. However, finding the model uncertainty level is challenging in many cases. Although the Bayesian NNs (BNNs)-based methods are popular for providing the confidence bound of the models, they cannot be employed in complex NN structures as they are computationally intractable. This paper uses the Monte Carlo (MC) dropout as an approximation version of BNNs. Then these uncertainty levels and each predictive model suggested by GA are used to generate a new model, which is then used for forecasting the TS and AD. Simulation results show that the forecasting and AD capability of the ensemble model outperforms existing approaches.
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates.Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning.Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks.We assume that the design of defense mechanism and attacks are two sides of the same coin.Designing a method to reduce vulnerability requires the attack to be effective and challenging with real-world implications.In this work, we propose simulated poisoning and inversion network (SPIN) that leverages the optimization approach for reconstructing data from a differential model trained by a vehicular node and intercepted when transmitted to roadside unit (RSU).We then train a generative adversarial network (GAN) to improve the generation of data with each passing round and global update from the RSU, accordingly.Evaluation results show the qualitative and quantitative effectiveness of the proposed approach.The attack initiated by SPIN can reduce up to 22% accuracy on publicly available datasets while just using a single attacker.We assume that revealing the simulation of such attacks would help us find its defense mechanism in an effective manner.
Federated Learning for Water Consumption Forecasting in Smart Cities

Water consumption remains a major concern among the world's future challenges.For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities.On the one hand, the information used is private.For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home.On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly.This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption.The proposed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility.In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity.Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.
Sensor Fusion and Extended Multi-Target Tracking in Joint Sensing and Communication Networks

In this paper, we consider a joint sensing and communication (JSC) network in which multiple base stations (BSs) cooperate through a fusion center (FC) to detect and track the objects present in a supervised area. Every BS acts as a monostatic sensor capable of scanning the environment and sensing the targets while simultaneously communicating with user equipments (UEs). In particular, each BS generates range-angle maps, which are shared with a FC for data fusion and tracking via particle filter (PF) and multi-hypothesis tracker (MHT) algorithms. The performance of the proposed solutions is evaluated by varying the fraction of power and time devoted to sensing to manage the network overhead and offer a sensing/communication trade-off. Numerical results show that the proposed algorithms can successfully track multiple targets with different sizes and behavior in a vehicular scenario, ensuring, e.g., a root mean squared error (RMSE) of the estimated position of a pedestrian less than 50 cm when considering three BSs.
Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.
Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission

Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC studies only focus on single-input single-output (SISO) channels. In this paper, we first propose a JSCC system for wireless image transmission over multiple-input multiple-output (MIMO) channels. As the complexity of an image determines its reconstruction difficulty, the JSCC achieves quite different reconstruction performances on different images. Moreover, we observe that the images with higher reconstruction qualities are generally more robust to the noise, and can be allocated with less communication resources than the images with lower reconstruction qualities. Based on this observation, we propose an adaptive channel state information (CSI) feedback scheme for precoding, which improves the effectiveness by adjusting the feedback overhead. In particular, we develop a performance evaluator to predict the reconstruction quality of each image, so that the proposed scheme can adaptively decrease the CSI feedback overhead for the transmitted images with high predicted reconstruction qualities in the JSCC system. We perform experiments to demonstrate that the proposed scheme can significantly improve the image transmission performance with much-reduced feedback overhead.
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming

In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) model for the cloud-edge collaborative transcoding. Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively. Two light-weight Bayesian neural networks are adopted to fit the TWE models in DTs, respectively. Moreover, we formulate a transcoding-path selection problem to maximize long-term user satisfaction within an average service delay threshold, taking the dynamics of video arrivals and video requests into account. The problem is transformed into a standard Markov decision process by using the Lyapunov optimization, which is further solved by a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed scheme can effectively enhance user satisfaction compared with benchmark schemes.
ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.
Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network

In distributed multiple-input multiple-output (D-MIMO) networks, power control is crucial to optimize the spectral efficiencies of users and max-min fairness (MMF) power control is a commonly used strategy as it satisfies uniform qualityof-service to all users.The optimal solution of MMF power control requires high complexity operations and hence deep neural network based artificial intelligence (AI) solutions are proposed to decrease the complexity.Although quite accurate models can be achieved by using AI, these models have some intrinsic vulnerabilities against adversarial attacks where carefully crafted perturbations are applied to the input of the AI model.In this work, we show that threats against the target AI model which might be originated from malicious users or radio units can substantially decrease the network performance by applying a successful adversarial sample, even in the most constrained circumstances.We also demonstrate that the risk associated with these kinds of adversarial attacks is higher than the conventional attack threats.Detailed simulations reveal the effectiveness of adversarial attacks and the necessity of smart defense techniques.
DistFL: An Enhanced FL Approach for Non Trusted Setting in Water Distribution Networks

The Internet of Things (IoT) is changing today's world, and Machine Learning (ML) is a major contributor to this revolution in terms of data exchange to mature connected objects. In this context, federated learning (FL) is emerging, a new ML paradigm that drives a model on decentralized data, which can be distributed across many IoT devices. FL has grown considerably in recent years, both in academia and industry. However, the majority of FL algorithms assume that all client nodes are honest and willing to participate in cooperative model learning. Thus, each node is able to provide reliable local models to the central server. However, in real-life scenarios, nodes may be corrupt, malicious, or both, and may not cooperate fairly during training phases. In this paper, we address the above challenge by proposing a new FL algorithm called DistFL. The main objective of DistFL is to prevent biased training by identifying malicious nodes during the training phase. We evaluate the effectiveness of our technique and demonstrate it through a concrete implementation, comparing DistFL with conventional FL. Even with up to 50% malicious nodes, the runtime cost of the DistFL model is still better than that of the conventional FL model, and its final accuracy reaches 97% with a loss function convergence rate twice that of the conventional FL model. For this study, we used urban water data to deal with leakage and distribution faults in the water network among different end users in this area.
Communication-Efficient Privacy-Preserving Federated Learning via Knowledge Distillation for Human Activity Recognition Systems

Emerging Internet of Things (IoT) applications, such as sensor-based Human Activity Recognition (HAR) systems, require efficient machine learning solutions due to their resource-constrained nature which raises the need to design heterogeneous model architectures. Federated Learning (FL) has been used to train distributed deep learning models. However, standard federated learning (fedAvg) does not allow the training of heterogeneous models. Our work addresses the model and statistical heterogeneities of distributed HAR systems. We propose a Federated Learning via Augmented Knowledge Distillation (FedAKD) algorithm for heterogeneous HAR systems and evaluate it on a self-collected sensor-based HAR dataset. Then, Kullback-Leibler (KL) divergence loss is compared with Mean Squared Error (MSE) loss for the Knowledge Distillation (KD) mechanism. Our experiments demonstrate that MSE contributes to a better KD loss than KL. Experiments show that FedAKD is communication-efficient compared with model-dependent FL algorithms and outperforms other KD-based FL methods under the i.i.d. and non-i.i.d. scenarios.
Vision Transformer for Adaptive Image Transmission over MIMO Channels

This paper presents a vision transformer (ViT) based joint source and channel coding (JSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) systems, called ViT-MIMO. The proposed ViT-MIMO architecture, in addition to outperforming separation-based benchmarks, can flexibly adapt to different channel conditions without requiring retraining. Specifically, exploiting the self-attention mechanism of the ViT enables the proposed ViT-MIMO model to adaptively learn the feature mapping and power allocation based on the source image and channel conditions. Numerical experiments show that ViT-MIMO can significantly improve the transmission quality across a large variety of scenarios, including varying channel conditions, making it an attractive solution for emerging semantic communication systems.
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-Enabled IoTs: An Anticipatory Study

Federated edge learning can be essential in supporting privacy-preserving, artificial intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) environments.However, we need to also consider the potential of attacks targeting the underlying AI systems (e.g., adversaries seek to corrupt data on the IoT devices during local updates or corrupt the model updates); hence, in this article, we propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.Specifically, we study the influence of adversaries on the training and development of federated learning models in digital twin 6Genabled IoT environments.We demonstrate that attackers can carry out poisoning attacks in two different learning settings, namely: centralized learning and federated learning, and successful attacks can severely reduce the model's accuracy.We comprehensively evaluate the attacks on a new cyber security dataset designed for IoT applications with three deep neural networks under the non-independent and identically distributed (Non-IID) data and the independent and identically distributed (IID) data.The poisoning attacks, on an attack classification problem, can lead to a decrease in accuracy from 94.93% to 85.98% with IID data and from 94.18% to 30.04% with Non-IID.
Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers

In the design of wireless receivers, deep neural networks (DNNs) can be combined with traditional model-based receiver algorithms to realize modular hybrid model-based/data-driven architectures that can account for domain knowledge. Such architectures typically include multiple modules, each car-rying out a different functionality. Conventionally trained DNN-based modules are known to produce poorly calibrated, typically overconfident, decisions. This implies that an incorrect decision may propagate through the architecture without any indication of its insufficient accuracy. To address this problem, we present a novel combination of Bayesian learning with hybrid model-based/data-driven architectures for wireless receiver design. The proposed methodology, referred to as modular model-based Bayesian learning, results in better calibrated modules, improving accuracy and calibration of the overall receiver. We demonstrate this approach for the recently proposed DeepSIC multiple-input multiple-output receiver, showing significant improvements with respect to the state-of-the-art learning methods.
Resource Allocation for Capacity Optimization in Joint Source-Channel Coding Systems

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarkar's algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.
Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study

With the proliferation of deep learning techniques for wireless communication, several works have adopted learningbased approaches to solve the channel estimation problem.While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time.Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training.This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches.In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model.We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion.We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings.Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.