Search Results (320)

Understanding user behavior on the web is considered essential for personalization, recommendation, and anomaly detection. Centralized analytics approaches raise significant privacy risks and regulatory concerns, particularly when large volumes of interaction data are collected in the cloud. Federated learning offers a decentralized alternative but faces challenges in handling heterogeneous, Non-Independently and Identically Distributed (non-IID) web interaction data. This paper presents FogLearn-Web, a fog computing-based federated learning framework for privacy-preserving web user behavior analytics. The architecture employs hierarchical aggregation in which browser-embedded models train locally, fog nodes perform behavior-aware regional aggregation, and the cloud maintains a global model with formal differential privacy guarantees. A key contribution is the behavioral sketch, a compact representation of local interaction distributions that enables attention-weighted federated averaging without exposing raw data. Experiments on benchmark and real-world datasets show that FogLearn-Web achieves within 2.3% of centralized accuracy while reducing data transmission by 89% and improving convergence under non-IID settings by 34% over standard FedAvg. Full article

As industries deploy more connected devices in factories, warehouses, and smart facilities, the need for artificial intelligence (AI) systems that can operate securely in distributed, data-intensive environments is growing. Traditional centralized learning and online education platforms struggle when students and systems have to process real-time streams (sensors, video, text) with strict latency and privacy requirements. To address this challenge, a blockchain-secured, edge-enabled multimodal federated learning framework tailored for Industrial IoT (IIoT) environments is proposed. The model integrates four key layers: (i) a blockchain layer that provides credentialing, transparency, and token-based incentives; (ii) a multimodal community layer that supports group formation, peer consensus, and cross-modal learning across text, images, audio, and sensor data; (iii) an edge computing layer that enables low-latency task offloading and secure training within Intel SGX enclaves; and (iv) a data layer that applies pre-processing, differential privacy, and synthetic augmentation to safeguard sensitive information. Experiments on industrial multimodal datasets demonstrate 42% faster model aggregation, 78.9% multimodal accuracy, and 1.9% accuracy loss under ε = 1.0 differential privacy. This shows a scalable and practical path for decentralized AI training in next-generation IIoT systems, confirming the possibility of technical support for educational processes. However, the conducted research requires a validation of pedagogical effectiveness. Full article

The proliferation of Enterprise Networks, characterized by heterogeneous devices, distributed data sources, and increasingly sophisticated cyber threats, has exposed the limitations of traditional perimeter-based security models. Guided by the principles of Zero Trust Architecture (ZTA), this paper presents a Zero-Trust (ZT)-Driven Federated Learning Algorithm for Privacy Enhancement (ZT-FL-PE), designed to safeguard model and data confidentiality in decentralized learning environments. By integrating ZTA’s “never trust, always verify” posture with Federated Learning’s (FL) decentralized training paradigm, the proposed framework eliminates the need for centralized data aggregation and significantly reduces the attack surface. The algorithm specifically targets two prominent threats to model privacy: property inference attacks (PIAs) and membership inference attacks (MIAs). We introduce adaptive verification mechanisms and privacy-preserving update transformations that enforce continuous authentication, constrain adversarial behavior, and strengthen resilience against inference-based exploitation. Experimental results demonstrate that ZT-FL-PE substantially enhances privacy protection while maintaining high model accuracy and imposing only low-to-moderate computational overhead, making it a practical and robust solution for modern ZT Enterprise environments. Full article

Federated learning has become a promising paradigm in the training of decentralized machine learning models across clients without sharing raw data, thereby preserving privacy. Current federated support vector machine methods are mainly based on the learning of a single global model, which inadequately addresses the challenges presented by heterogeneous and non-IID client data distributions. To overcome these limitations, we propose FMT-SVM, a novel federated multi-task learning framework that jointly trains both binary and multi-class classification tasks within each client, where the client uses a unified convolutional neural network encoder to extract common features among tasks, which are passed to task-specific linear SVM heads dedicated to each classification task. By leveraging a primal optimization integrating task covariance and global consensus regularization, FMT-SVM explicitly models relationships between heterogeneous tasks and enforces alignment across clients, effectively handling the non-IID nature of data distributions. Unlike traditional FL methods that exchange entire model parameters or large support vector sets, our method communicates only the compact SVM heads during aggregation, greatly reducing communication overhead and enhancing scalability for clients with limited bandwidth. To further enhance privacy, Gaussian differential privacy mechanisms are applied to client updates, balancing privacy preservation with predictive performance. Experiments are performed on two medical image datasets: the Pediatric Pneumonia Dataset and the Breast Ultrasound dataset, demonstrating that the FMT-SVM framework achieves competitive accuracy on both binary and multi-class tasks while maintaining communication efficiency and privacy guarantees. These results highlight the capability of the proposed FMT-SVM framework as a practical, scalable, and privacy-aware solution for the federated true multi-task learning problem in sensitive healthcare applications. Full article

Consumer wellbeing systems are characterized by conceptual fragmentation, heterogeneous data sources, and multilevel interactions across economic, psychological, social, and environmental domains. Existing monitoring approaches remain largely unidimensional and lack integrative system architectures capable of supporting real-time, adaptive analysis. This paper proposes a Federated Digital Twin (FDT) framework for Consumer Wellbeing Systems, designed to integrate decentralized, multimodal data while preserving autonomy and privacy. The proposed architecture builds on a five-dimensional digital twin model and extends it through federated interoperability, data fusion, adaptive learning, simulation capabilities, and human-in-the-loop mechanisms. The framework enables the synchronization of observed, self-reported, contextual, and synthetic data across distributed environments, supporting system-level modeling, prediction, and optimization. As an illustrative application, the paper examines Shopping Wellbeing and Shopping–Life Balance as sub-systems within broader wellbeing ecosystems, demonstrating how federated digital twins can unify fragmented theoretical constructs into a coherent, dynamic monitoring structure. The study contributes a system-oriented conceptual architecture for modeling complex human-centric wellbeing ecosystems and outlines implications for systems design, governance, and future interdisciplinary research. Full article

The rapid advancement of artificial intelligence relies on massive high-quality data, yet increasingly stringent data privacy regulations have exacerbated the problem of data silos. Federated learning enables collaborative training under privacy protection by exchanging model parameters rather than transmitting raw data. Nevertheless, its traditional centralized architecture still suffers from limitations such as single points of failure, lack of trust, and insufficient incentives. The integration of blockchain and federated learning opens new pathways for decentralized, auditable, and secure machine learning systems. This paper systematically reviews research progress in blockchain-enabled federated learning, analyzing technological evolution from three perspectives: system architecture, incentive mechanisms, and privacy enhancement. It further explores critical challenges including efficiency bottlenecks, storage overhead, and the inherent tension between transparency and privacy, while identifying key research directions for building scalable, efficient, and trustworthy decentralized learning systems. Full article

This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, governance frameworks, and real-world applications. A systematic literature review was conducted, analyzing peer-reviewed studies from PubMed, Scopus, and Web of Science to map the current landscape of the field. The primary objective was to synthesize the current research on decentralized approaches in healthcare, including core approaches like federated learning and blockchain-based AI models, as well as emerging concepts such as agentic AI blockchain-based AI models and DAOs, to comprehend their application in clinical and operational settings. The research assesses the maturity of these implementations, ranging from pilot programs to large-scale organizational settings. It also identified the key computational and technical methods and platforms used and the key benefits and challenges influencing their adoption. The findings underscore the pivotal role of the decentralized paradigm in addressing the fundamental limitations of traditional AI, including data privacy, trust, institutional silos, and regulatory complexity. Insights are also offered for healthcare providers, technology developers, researchers, and policymakers aiming to navigate and leverage decentralized AI to build more equitable, efficient, and collaborative healthcare systems. Full article

Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing DP-FL approaches rely on fixed global clipping bounds for client updates, which substantially overestimate sensitivity when privacy loss is composed using Rényi Differential Privacy (RDP), zero-Concentrated DP (zCDP), or Moments Accountant (MA) frameworks, leading to excessive noise and degraded utility. This work proposes an adaptive clipping-based RDP accountant that incorporates empirical, round-wise update magnitudes into privacy accounting by rescaling each round’s RDP contribution according to the observed clipping ratio. The method is optimizer-agnostic and is evaluated with FedAvg, FedProx, and SCAFFOLD on the SGCC smart-meter theft dataset under IID and Dirichlet non-IID partitions. Experimental results show consistently tighter privacy bounds and improved model utility compared to classical DP accountants, demonstrating the effectiveness of sensitivity-aware privacy accounting for practical differentially private FL. Full article

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently understood in IoT malware detection. This study provides a controlled comparative analysis of centralized and federated learning, optionally using amino acid encoding, under IID and Non-IID conditions using a 10,000-sample subset of the CTU–IoT–Malware–Capture dataset. First, we evaluate raw tabular features versus amino acid-based feature encoding, followed by a lightweight multi-layer perceptron (2882 parameters) versus a deeper residual network (70,532 parameters), across binary and multi-class classification tasks. In the binary setting, centralized training achieved up to 98.6% accuracy, while federated IID training reached 98.6%, with differences within statistical variance. Under Non-IID conditions, performance decreased modestly (0.1–0.5 percentage points), and accuracy was consistently lower when using encoded features compared with raw features. The degradation is smaller in deeper architectures and may offer improved stability under highly skewed federated conditions. In the four-class setting, the complex network achieved up to 97.8% accuracy with raw features, while amino acid encoding achieves up to 93.3%. The results show that federated learning can achieve performance comparable to centralized training under moderate heterogeneity, that lightweight architectures are sufficient for low-dimensional IoT traffic features, and that feature compression via amino acid encoding does not inherently mitigate Non-IID effects. These findings clarify the relative impact of representation, heterogeneity, and architectural capacity in practical FL-based IoT intrusion detection systems. Full article

Predicting loan defaults is a significant challenge for financial institutions; however, current machine learning techniques often encounter issues in areas such as data privacy, cross-institutional cooperation, and model transparency. The restrictions on the practical implementation of advanced predictive models are centralized training paradigms, which limit the application of advanced models because of regulatory and confidentiality issues, and black-box decision making, which diminishes confidence in automated credit risk tools. This study mitigates these problems by adopting a federated-inspired decentralized ensemble learning model combined with explainable artificial intelligence (XAI) in predicting loan defaults. Various machine learning classifiers are trained on partitioned institutional data without the need to share any data; they include K-Nearest Neighbors, support vector machine, random forest, and XGBoost. They use a prediction-level aggregation strategy to simulate the collaborative decision-making process without losing locality of data. SHAP and LIME are used to promote model interpretability by giving both global and local explanations of the consequences of prediction. The proposed framework was tested on a large public dataset of loans that contains more than 116,000 records, including various financial and borrower-related features. The experimental findings show that XGBoost has high and reliable predictive accuracy in both centralized and decentralized scenarios, achieving 99.7% accuracy under federated-inspired evaluation. The explanation analysis shows interest rate spread and upfront charges as the most significant predictors of loan default risk. The main contributions of this research are as follows: (i) a privacy-preserving decentralized ensemble learning framework that is applicable in multi-institutional financial contexts, (ii) a detailed analysis of centralized and decentralized predictive performances, and (iii) the pipeline of the XAI, which can be used to increase its transparency and regulatory confidence in automated credit risk evaluation. These results prove that decentralized learning combined with explainable AI can provide high-performing, transparent and privacy-sensitive loan default prediction systems in practice in real-world banking systems. Full article

The sophistication of cyber attacks and privacy issues related to data sharing is improving and requires a decentralized approach. Conventional centralized approaches to IDS pose a threat to the privacy of data and data sovereignty. Contrarily, federated learning enables several clients to learn simultaneously without sharing their sensitive information, which is one of the most promising solutions to studying cyber threats in real time. This framework also adds value to IDS by using CTI, which is incorporated into the training process to make it more accurate in its detection while still maintaining privacy. Each client uses the local model, which is a random forest model that is trained on local datasets without sharing the raw data. Multiple aggregation methods, such as FedAvg, FedOPT, FedProx, and FedXGBoost, are then used to combine the local models into a global model. These techniques are judged with regard to accuracy and Cohen’s Kappa Score. The performance of various models in the NF-UNSW-NB15-v2 dataset experiments was tested. The local model took a value of 0.9941–0.9934 with Kappa scores of 0.8336–0.8088, showing strong performance in different configurations. The FedXGBoost aggregated global model was best in terms of its highest accuracy of 99.22 (Kappa score of 0.8417). More experiments were done on the DFedForest and DFedForest++ models. DFedForest++, incorporating diversity in local models alongside validation accuracy, achieved 99.76% accuracy, surpassing DFedForest (with 71% accuracy in local models). This framework operationalizes CTI through feature augmentation—appending three CTI-derived features (is_known_malicious_ip, is_suspicious_port, and ttp_match_score from MITRE ATT&CK v14 and AlienVault OTX) to each NetFlow record locally at each client before federated training begins. These results highlight the advantages of federated learning in providing collaborative, privacy-preserving solutions for cyber threat detection and emphasize the potential of CTI integration for improving the accuracy and robustness of IDS models across decentralized environments. Full article

Federated recommender systems (FRS) enable privacy-preserving collaborative training without sharing raw user data, while explainable recommender systems (XRS) aim to improve transparency, trust, and accountability. However, research that integrates federation and explainability remains limited and fragmented. This survey reviews recent work at the intersection of Federated Learning (FL), Explainable Artificial Intelligence (XAI), and recommender systems, referred to as Explainable Federated Recommender Systems (XFRS). We analyze architectures, learning paradigms, personalization strategies, and explainability mechanisms, and discuss their trade-offs in explainability, privacy, and trustworthiness. We propose a unified conceptual framework that links these components in decentralized recommendation settings. Combining bibliometric analysis with a systematic categorization of the literature, we identify key gaps and emerging trends, including the limited adoption of explainability in federated settings. Finally, we summarize open challenges and future directions toward trustworthy, privacy-aware personalized recommender systems. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article

Distributed hydrogen refueling stations enable the coupling of renewable generation, storage, and demand fulfillment; however, their performance depends on coordinated control under strict physical safety limits. Centralized controllers are often impractical due to privacy constraints and unreliable communication links, while unconstrained learning can reduce operating costs at the expense of unsafe pressure excursions. Therefore, this study evaluates safety-constrained coordination across multiple stations using federated learning-based distributed scheduling and benchmarks a non-federated Local Control baseline (local-only, no coordination). Using a feasibility-first rule with an acceptance threshold of $\tau =0.2$ on the pressure violation metric ($V_p\le 0.2$), the best feasible overall controller (Local Control) achieved a cost of 2131.83 with pressure violation $V_p=0.172$, representing a 37.22% reduction relative to a centralized reference cost of 3396.25. Federated training with Federated Averaging and a solar–wind mixing scheme produced the best feasible federated policy (cost 2423.72, $V_p=0.163$) with 866,688 transmitted bytes. Extensive simulations report cost, unmet demand, safety violations, and communication overhead, demonstrating that feasibility-first selection is essential because lower-cost policies can be unsafe (e.g., cost 1952.27 with $V_p=2.63$). Full article

In this paper, we propose FDSTCN-EEG, which is a customized federated learning framework for EEG-based seizure detection that leverages deep depthwise separable temporal convolutions and asynchronous model aggregation. The network design tackles major problems in distributed healthcare AI by jointly boosting computational efficiency, training rate, and classification performance. In this paper, we propose FDSTCN-EEG, a novel federated learning framework specifically designed for EEG-based seizure detection. Our key contributions are threefold: first, high architectural efficiency with depthwise separable temporal convolutions, reducing parameters by 40.4% (9.8M to 5.8M) while maintaining accuracy of 96.96%; second, speeding up training by a factor of 38.5% compared with synchronous learning via an asynchronous aggregation protocol; finally, a privacy-preserving decentralized learning model without sharing raw EEG data and with the capability of coping with the heterogeneous clinical technology infrastructure. Extensive experiments show superior performance (accuracy: 96.96%, F1-score: 97.02%) and practical viability for real-world seizure monitoring systems. Such work introduces a practical privacy-preserving medical AI paradigm, which balances model efficiency with training scalability and clinical quality accuracy. Full article