Mitigating adversarial attacks in federated learning based network traffic classification applications using secure hierarchical remote attestation and adaptive aggregation framework

Edge servers and mobile devices are often assigned a large number of computing tasks. However, the data involved in computing tasks is often sensitive in terms of privacy. Our initial proposal is a federated edge learning strategy based on real-world scenarios, which combines blurred data or pseudo shared data. Federated learning is used to train device models with the aim of protecting privacy while enabling mobile devices to more effectively utilize data for decision-making. In the case of limited energy on mobile devices, we propose a federated edge learning algorithm with blurred data sharing. This algorithm can generate more accurate models by uploading partially blurred data. In order to further improve model accuracy and protect privacy of mobile devices, we propose a federated edge learning algorithm with pseudo data sharing based on dataset distillation and generative adversarial networks (GANs) in scenarios with relatively sufficient energy. The experimental results on several traditional datasets show that our proposed algorithms outperform traditional algorithms in terms of accuracy and energy consumption.

Benefited from privacy improvement during cooperative training, federated Learning (FL) has been popularly applied for intelligent manufacturing within industrial internet of things (IIoT). However, recent studies have shown the vulnerability of existing FL, since the privacy provided by FL might be inferenced on the wireless training stage by interactive data. More seriously, for industrial applications, traditional FL methods of protecting privacy might lead to a drastic decrease in accuracy, caused by the increasing difficulty and costs of managing massive clients. Thus, this paper proposes an Adaptive Noise Trap (ANT). As a protection strategy for privacy applied in FL, our additive noise defense strategy is able to address the challenges posed by massive privacy in the data level and reduce its defense cost by adding noise defense on the server-side global model in FL. Meanwhile, ANT adjusts the noise size according to the privacy carried in the model, which ensures the privacy defense performance and model accuracy in large-scale IIoT. Especially, network traffic classification is chosen as a typical application scenario for the methodology verifications based on simulations. Through experimental comparisons, ANT achieves a lightweight defense, which uses less noise to achieve the desired defense effect. This lightweight defense can play an unimaginable role in the massive scale of IIoT. Moreover, the simulation results also show that ANT guarantees convergence, accuracy and reliability of federated learning.

The expansion of the Internet of Medical Things (IoHT) presents significant advantages for healthcare over improved data-driven insights and connectivity and offers critical cybersecurity challenges. Attacks are a serious risk for neural network security; recent defence mechanisms remain restricted concerning their applicability to real-world environments. The influence of adversarial attacks is essential, as they can challenge the security and reliability of Artificial Intelligence (AI) methods in crucial applications. Dealing with these vulnerabilities is vital to develop strong and reliable NNs. Therefore, the study of adversarial defence mechanisms and attack detection became an important area in the domain of AI. Machine learning (ML) and specific deep learning (DL) models have recently influenced excellent performance on challenging perceptual tasks like adversarial attack detection. Meanwhile, the federated learning (FL) method is susceptible to attacks by malicious clients. FL can complete a considerable training task effectively by attracting participants for training a DL method cooperatively, and the user privacy should be completely protected for the users only upload model parameters to the centralized server. This study presents an Adversarial Attack Detection Framework Using Federated Learning Empowered IoT Medical (AADF-FLEIoTM) model. The main intention of the AADF-FLEIoTM model is to develop adversarial attack detection using FL and an advanced hybrid model. The data normalization stage initially uses min-max normalization to scale and transform data into a consistent range. The proposed AADF-FLEIoTM employs the marine predator algorithm (MPA) model to identify and retain the most relevant features for the feature selection process. Besides, the integration of convolutional neural networks, bidirectional long short-term memory, and self-attention (SA-CNN-BiLSTM) technique is utilized for the detection and classification process. Finally, the Red-Tail Hawk (RTH)-optimizer algorithm alters the hyperparameter values of the SA-CNN-BiLSTM technique optimally and results in more excellent classification performance. The AADF-FLEIoTM approach is examined on the IoT healthcare security dataset. The performance validation of the AADF-FLEIoTM approach illustrated a superior accuracy value of 98.24% over existing models.

Internet of Things (IoT) systems are rightly receiving considerable interest for many real-world applications, from in-body networks to satellite networks. Such a massive-scale system generates a considerable amount of traffic data, making IoT systems a distributed data source generator. For many reasons, such as the functionality of IoT applications and Quality of Service (QoS) provisioning, classifying these traffic data is of high importance. In the last few years, widespread interest has been expressed in applying Machine Learning (ML)-based techniques for Network Traffic Classification (NTC) tasks. However, the traditional centralized learning-based traffic classifiers pose serious challenges, especially in IoT networks. The centralized ML techniques call for collecting a large amount of data from various IoT devices, which in turn introduces data governance and privacy challenges. Furthermore, in the centralized ML, training data need to be transferred to the Cloud, which increases communication cost and latency. To address these problems, we propose Federated Learning (FL) Internet of Things (IoT) Traffic Classifier (FLITC)-a Federated Learning (FL)-based IoT traffic classification method which is based on the Multi-Layer Perception (MLP) neural network and holds the local data unimpaired on IoT devices by sending only the learned parameters to the aggregation server. Our experimental results show that the FLITC beats centralized learning in preserving the privacy of sensitive data and offers a better degree of accuracy at the cost of a longer training time.

Accelerating Federated Learning with genetic algorithm enhancements

<abstract> <p>Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.</p> </abstract>

Federated Learning (FL) allows several data owners to train a joint model without sharing their training data. Such a paradigm is useful for better privacy in many ubiquitous computing systems. However, FL is vulnerable to poisoning attacks, where malicious participants attempt to inject a backdoor task in the model at training time, along with the main task that the model was initially trained for. Recent works show that FL is particularly vulnerable to edge-case backdoors introduced by data points with unusual out-of-distribution features. Such attacks are among the most difficult to counter, and today's FL defense mechanisms usually fail to tackle them. In this paper, we present ARMOR, a defense mechanism that leverages adversarial learning to uncover edge-case backdoors. In contrast to most of existing FL defenses, ARMOR does not require real data samples and is compatible with secure aggregation, thus, providing better FL privacy protection. ARMOR relies on GANs (Generative Adversarial Networks) to extract data features from model updates, and uses the generated samples to test the activation of potential edge-case backdoors in the model. Our experimental evaluations with three widely used datasets and neural networks show that ARMOR can tackle edge-case backdoors with 95% resilience against attacks, and without hurting model quality.

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL's limitations. Successfully addressing these hurdles is essential for enhancing FL's efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem.

Federated learning (FL) has been used to several domains, such as smart cities, intelligent healthcare systems, and sectors reliant on the internet of things (IoT). This is in response to the growing concern about privacy and security in machine learning applications. Clients may work together to train a global model using FL, even when they don't have access to the same data as before. Nevertheless, adversarial attacks may exploit the weaknesses of existing FL methods. Because of its design, protecting against harmful model changes and identifying them are both made difficult. Not enough has been investigated in the most current research on protecting the model's privacy while detecting FL from harmful updates. This study presented a solution to the problem of poisoning assaults on healthcare systems: federated learning based on blockchain technology with SMPC model verification. We start by removing the compromised model after verifying it via an encrypted inference procedure using the FL participants' machine learning models. After each participant's local model is checked, it is safely forwarded to the blockchain node for aggregation. In order to test our proposed framework, we used a variety of medical datasets in a number of studies.

Federated Learning (FL) enables collaborative model training without exposing sensitive data, making it a cornerstone for privacy-aware AI. However, bringing FL from theory to practice in multi-tier architectures that contain hierarchical edge-fog-cloud systems remains difficult, challenged by security vulnerabilities, resource constraints, and system heterogeneity. This paper reviews secure and adaptive optimization techniques that address these challenges in hierarchical environments. We focus on three main areas: (1) security-enhanced FL, covering threat models, adversarial attacks, and defenses tailored to layered architectures; (2) adaptive optimization, including dynamic client selection, resource-aware aggregation, and context-sensitive privacy management; and (3) systems integration, examining architectural designs, communication protocols, and orchestration methods for scalable deployment. By synthesizing recent work, the survey highlights trade-offs between privacy, performance, and scalability, and proposes a taxonomy of current challenges and solutions. It also evaluates approaches to trustworthiness in FL, including fairness, accountability, and robustness, while considering practical issues such as regulation, benchmarking, and deployment constraints. The paper concludes with open research directions, emphasizing the need for secure, adaptive, and production-ready FL systems.

Federated learning (FL), as a distributed learning mechanism, can have model training completed without directly uploading original data, effectively reducing the risk of privacy leakage. However, through the shared gradient information, research shows that adversaries may reconstruct the original data. To further protect the privacy of federated learning, a federated learning defense scheme is proposed based on generative adversarial networks (GAN), which is combined with adaptive differential privacy. Firstly, the real data distribution features are learned through GAN, and replaceable pseudo data are generated. Then, the pseudo data are added with adaptive noise. Finally, the pseudo gradient generated by the pseudo data in the model is used to replace the real gradient so that adversaries cannot obtain the real gradient to further protect the privacy of user data. After simulation experiments are carried out on the MNIST dataset, the algorithm is verified using the gradient attack method. The experimental results show that the proposed algorithm is superior to the federated learning algorithm based on differential privacy in accuracy. Compared with the FedAvg algorithm, only 0.48% accuracy is lost. Therefore, it achieves a good balance between algorithm accuracy and data privacy.

Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to adversarial attacks, similar to other AI models. In this paper, we propose federated adversarial training (FAT) strategies to generate robust global models that are resistant to adversarial attacks. We apply two adversarial attack methods, projected gradient descent (PGD) and the fast gradient sign method (FGSM), to our air pollution dataset to generate adversarial samples. We then evaluate the effectiveness of our FAT strategies in defending against these attacks. Our experiments show that FGSM-based adversarial attacks have a negligible impact on the accuracy of global models, while PGD-based attacks are more effective. However, we also show that our FAT strategies can make global models robust enough to withstand even PGD-based attacks. For example, the accuracy of our FAT-PGD and FL-mixed-PGD models is 81.13% and 82.60%, respectively, compared to 91.34% for the baseline FL model. This represents a reduction in accuracy of 10%, but this could be potentially mitigated by using a more complex and larger model. Our results demonstrate that FAT can enhance the security and privacy of sustainable smart city applications. We also show that it is possible to train robust global models from modest datasets per client, which challenges the conventional wisdom that adversarial training requires massive datasets.

<span lang="EN-US">Edge artificial intelligence (Edge AI) involves the implementation of AI algorithms and models directly on local edge devices, such as sensors or internet of things (IoT) devices. This allows for immediate processing and analysis of data without the need for continuous dependence on cloud infrastructure. Concerns about privacy have grown importance in recent years for businesses looking to uphold end-user expectations and safeguard business models. Federated learning (FL) has emerged as a novel approach to enhance privacy. To improve generalization qualities, FL trains local models on local data. These models then collaborate to update a global model. Each edge device (like smartphones, IoT sensors, or autonomous vehicles) trains a local model on its own data. This local training helps in capturing data patterns specific to each device or node. Poisoning, backdoors, and generative adversarial network (GAN)-based attacks are currently the main security risk. Nevertheless, the biggest threat to FL’s privacy is from inference-based assaults such as model inversion attacks, differential privacy shortcomings and FL utilizes blockchain and cryptography technologies to improve privacy on edge devices. This paper presents a thorough examination of the current literature on this subject. <br /> In more detail, we study the background of FL and its different existing applications, types, privacy threats and its techniques for privacy preservation.</span>

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource. While in the downlink of each FL iteration, both groups simultaneously receive data from the base station in the same time-frequency resource, the uplink of each FL iteration requires bidirectional communication to support uplink transmission for FL users and downlink transmission for non-FL users. To overcome this challenge, we present half-duplex (HD) and full-duplex (FD) communication schemes to serve both groups. More specifically, we adopt the massive multiple-input multiple-output technology and aim to maximize the minimum effective rate of non-FL users under a quality of service (QoS) latency constraint for FL users. Since the formulated problem is nonconvex, we propose a power control algorithm based on successive convex approximation to find a stationary solution. Numerical results show that the proposed solutions perform significantly better than the considered baselines schemes. Moreover, the FD-based scheme outperforms the HD-based counterpart in scenarios where the self-interference is small or moderate and/or the size of FL model updates is large.

The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT.