Federated-Learning-Empowered Semi-Supervised Active Learning Framework for Intrusion Detection in ZSM

Federated transfer learning-based intrusion detection system in 5G networks

A semi-supervised deep learning approach for cropped image detection

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

Gathering labeled data is one of the most time-consuming and expensive tasks in supervised machine learning. In practical applications, there are usually quite limited labeled training samples but abundant unlabeled data that is easy to collect. Semi-supervised learning and active learning are two important techniques for learning a discriminative classification model when labeled data is scarce. However, unlabeled data with significant noises and outliers cannot be well exploited and usually worsen the performance of semi-supervised learning and the performance of active learning also needs a powerful initial classifier learned from the quite limited labeled training data. In order to solve the above issues, in this paper we proposed a novel model of semi-supervised dictionary active learning (SSDAL), which aims to integrate semi-supervised learning and active learning to effectively use all the training data. In particular, two criterions based on estimated class possibility are designed to select the unlabeled data with confident class estimation for semi-supervised learning and the informative unlabeled data for active learning, respectively. Extensive experiments are conducted to show the superior performance of our method in classification applications, e.g., handwritten digit recognition, face recognition and large-scale image classification.

Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data. While FL ensures data privacy through collaborative learning, it faces several critical challenges. These include vulnerabilities to reverse engineering, risks to model architecture privacy, susceptibility to model poisoning attacks, threats to data integrity, and the high costs associated with communication and connectivity. This paper presents a comprehensive review of FL, categorizing data partitioning formats into horizontal federated learning, vertical federated learning, and federated transfer learning. Furthermore, it explores the integration of FL with blockchain, leveraging blockchain’s decentralized nature to enhance FL’s security, reliability, and performance. The study reviews existing FL models, identifying key challenges such as privacy risks, communication overhead, model poisoning vulnerabilities, and ethical dilemmas. It evaluates privacy-preserving mechanisms and security strategies in FL, particularly those enabled by blockchain, such as cryptographic methods, decentralized consensus protocols, and tamper-proof data logging. Additionally, the research analyzes regulatory and ethical considerations for adopting blockchain-based FL solutions. Key findings highlight the effectiveness of blockchain in addressing FL challenges, particularly in mitigating model poisoning, ensuring data integrity, and reducing communication costs. The paper concludes with future directions for integrating blockchain and FL, emphasizing areas such as interoperability, lightweight consensus mechanisms, and regulatory compliance.

Federated Learning (FL) is a Machine Learning (ML) paradigm in which multiple devices collaboratively train a model without sharing their local data. This decentralized approach provides significant privacy benefits, enabling compliance with data protection regulations and safeguarding sensitive user information by keeping raw data on local devices. Instead of transmitting raw data, FL sends model updates to a central aggregator to improve the global model. However, this process can result in higher Carbon Dioxide (CO₂) emissions compared to traditional centralized ML systems, due to the increased number of participating devices and communication rounds. This study evaluates the performance, convergence speed, energy efficiency, and environmental impact of FL models compared to centralized models, using the Modified National Institute of Standards and Technology dataset (MNIST) and Canadian Institute for Advanced Research-10 classes dataset (CIFAR-10). Four models were tested: two FL models and two centralized models. The evaluation focused on accuracy, number of training rounds to convergence, and total CO₂ emissions. To optimize both convergence and energy efficiency, a dynamic hill-climbing-based early stopping technique was introduced. After every 100 rounds, model accuracy improvements were assessed, and training was terminated early if further gains fell below a shrinking threshold, effectively reducing unnecessary computation and energy consumption. Results show that, under the tested conditions, FL models achieved competitive or higher accuracy than centralized models, particularly on non-Independent and Identically Distributed (IID) data distributions. For example, the federated MNIST model reached 98.79% accuracy with a significantly lower carbon footprint when early stopping was applied. Overall, the proposed optimization approach reduced CO₂ emissions by approximately 60% without substantial loss in accuracy. By integrating privacy preservation, explicit regulatory relevance, and a practical dynamic optimization method, this research demonstrates that FL can deliver strong model performance while meeting modern requirements for data privacy and environmental sustainability

Employing unlabeled data to improve the classification performance of SVM, and its application in audio event classification

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets.

Overcoming surveillance gaps: Deep learning for accurate detection and chronicity classification of hospital-acquired pulmonary embolism

Simple SummaryTracking the movements of chickens is important for understanding their well-being. Traditional methods for measuring chicken mobility are time-consuming and cannot provide real-time information. In this study, we, the researchers, used a combination of artificial intelligence methods and computer algorithms to track individual chickens. Using these methods, it was possible to detect and track individual chickens in two pens. We analyzed the data to see how far and how fast each chicken moved every hour and every day. Compared to manual measurements, the combined model provided more accurate measurements of the mobility of each chicken and the entire group, even when some chickens were hidden, or the detection was not perfect. This study may serve to effectively track indicators critical for broiler production performance and welfare.Mobility is a vital welfare indicator that may influence broilers’ daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model’s mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers’ hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.

Federated Learning (FL) is believed as a promising manner of distributed machine learning for 5G and future 6G networks in the context of mobile edge computing (MEC). From the worker’s viewpoint, a problem is that no incentive drives them to participate in FL. Thus, different from the conventional server-centric model allocation methods, we study the worker-centric strategy in this paper. Because communicating with the FL server and training an FL model locally are energy-hungry, a dilemma for each worker is how to make the tradeoff between participating in FL training and the volume restriction of its battery. To address this issue, we formulate a WorkerFirst problem. Its NP-hardness is also proved. Next, we devise a DDQN algorithm and a DQL algorithm to strive for near-optimal decisions for each worker. These two algorithms are proposed by leveraging the deep reinforcement learning framework, while considering the energy consumption, training timespan, and the communication overheads of workers, simultaneously. The benefit of adopting such a DRL-based decision-making framework is that workers can execute the algorithms locally and dynamically adapt to the varying MEC environment. The evaluation results show that the proposed DDQN and DQL algorithms can learn good policies without knowing any prior knowledge of network conditions.

The rapid evolution of Next Generation Networks (NGNs) has brought unprecedented capabilities in connectivity, automation, and data-driven services. However, the distributed and heterogeneous nature of NGNs—spanning cloud, edge, and IoT domains—poses significant challenges to maintaining robust cybersecurity without compromising data privacy or system latency. Traditional centralized security models struggle to cope with massive data volumes, privacy constraints, and real-time threat detection requirements. To address these challenges, this paper proposes the integration of Federated Learning (FL) into NGN security architectures, enabling distributed intelligence across network nodes while preserving data locality. In the proposed framework, participating edge devices and network entities collaboratively train a shared intrusion detection or anomaly detection model without exchanging raw data. This approach enhances data privacy, scalability, and adaptability to emerging threats. Furthermore, it mitigates the risk of single-point failures and bandwidth bottlenecks inherent in centralized learning systems. Experimental results and simulations demonstrate that FL-based NGN security models can achieve comparable or superior detection accuracy to centralized methods while significantly reducing data transmission overhead. The proposed system architecture leverages secure aggregation, robust model updates, and blockchain-based trust mechanisms to ensure integrity and resilience against model poisoning and adversarial attacks. This study highlights Federated Learning as a transformative paradigm for distributed NGN security, paving the way for intelligent, privacy-preserving, and self-adaptive defense mechanisms in future 5G and 6G networks. Keywords: Federated Learning, Next Generation Networks, Distributed Security, Edge Intelligence, Intrusion Detection, Privacy Preservation, Model Aggregation, 5G/6G.

Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while addressing challenges related to data privacy and security. FL enables collaborative learning without sharing sensitive medical data, making it an attractive solution for healthcare applications. This systematic review aims to analyze the advancements in AI-driven ocular disease detection, with a particular focus on FL-based approaches. The article evaluates the evolution, methodologies, challenges, and effectiveness of FL in enhancing diagnostic accuracy while ensuring data confidentiality. The systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and reliability. Research articles published between 2017 and 2024 were identified using academic databases, including Web of Science, Scopus, IEEE Xplore, and PubMed. Studies focusing on DL and FL models for detecting ocular diseases were selected based on predefined inclusion and exclusion criteria. A comparative analysis of the methodologies, architectures, datasets, and performance metrics of different FL models has been presented. The findings indicated that FL preserves data privacy while achieving diagnostic performance comparable to traditional centralized AI models. Various FL models, including FedAvg and FedProx, have been implemented for ocular disease detection, with high accuracy and efficiency. However, challenges, such as data heterogeneity, communication efficiency, and model convergence, persist. FL represents a promising approach for ocular disease detection, balancing diagnostic accuracy with data privacy. Future research may focus on optimizing FL frameworks for improving scalability, communication efficiency, and integrating advanced privacy-preserving techniques.

Federated learning (FL) has emerged as a powerful technology widely applied in Internet of Things (IoT). Recently, researchers have shown an increased interest in privacy-preserving FL with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . The goal of such works is to achieve private training under ciphertext mode while ensuring that the FL model is mainly derived from the contributions of users with high-quality data. However, the existing work is still in its infancy, and the main challenge faced by many researchers is how to achieve their schemes for meeting the demands of high accuracy and efficiency. To combat that, we propose an efficient privacy-preserving FL (EPPFL) scheme with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . Specifically, we design a novel scheme to mitigate the negative impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> , where the targeted model is guaranteed to be updated with high-quality data. Through iteratively executing our “Excluding Irrelevant Components” and “Weighted Aggregation,” the FL model converges rapidly while taking limited communication and computation overhead. As a result, not only the model accuracy can be optimized, but also the training efficiency can be improved. Meanwhile, we conduct a secure framework based on the threshold Paillier cryptosystem, which can rigorously protect all user-related private information during the training process. Furthermore, the extensive experiments demonstrate our EPPFL with high-level performance in terms of accuracy and efficiency.

Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.