Search Results (316)

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

The Internet of Medical Things (IoMT) transforms healthcare through interconnected medical devices but faces significant cybersecurity threats, particularly intrusion and exfiltration attacks. Centralized intrusion detection systems (IDSs) require data aggregation, presenting privacy and scalability risks. This paper proposes FedEnsemble-DP, a privacy-aware Federated Learning (FL) framework for decentralized intrusion detection in IoMT networks. The framework integrates three data balancing scenarios (Raw Imbalanced, Local SMOTE, Centralized SMOTE) with Differential Privacy (DP) and Secure Aggregation mechanisms. Extensive experiments on WUSTL-EHMS-2020 and CIC-IoMT-2024 datasets under non-IID settings (Dirichlet $\alpha$ = 0.3) demonstrate that models with strong privacy guarantees ($\epsilon$ = 3.0) frequently match or exceed non-private baselines. Key findings show Local SMOTE with $\epsilon$ = 3.0 achieved 94.60% accuracy and 0.9598 AUC, while Raw Imbalanced with $\epsilon$ = 3.0 attained 94.50% accuracy and 0.9494 AUC. Even with strict privacy ($\epsilon$ = 3.0), these results surpassed the non-private baseline (93.20% accuracy) in the raw scenario. Centralized SMOTE showed effectiveness but introduced training instability. These results indicate that local data balancing combined with calibrated DP noise can yield high detection performance while preserving privacy, effectively bridging security-performance and data confidentiality requirements in distributed healthcare networks. Full article

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article

Traffic flow prediction (TFP) is a fundamental component of intelligent transportation systems (ITS) that supports traffic management, congestion mitigation, and route planning. Although recent advances in deep learning have demonstrated strong capability in modeling non-linear spatio-temporal correlations, most existing approaches rely on centralized training paradigms, which incur substantial communication costs, high computational overhead, and significant data privacy risks. Federated Learning (FL) has emerged as a promising alternative by enabling decentralized model training across distributed clients while reducing privacy risks and communication overhead. However, existing FL-based TFP frameworks often employ local models with limited capacity to capture complex spatio-temporal dependencies, and their reliance on the conventional FedAvg aggregation approach restricts robustness under heterogeneous traffic data distributions. To address these challenges, this study proposes the FedASTAM framework, which integrates FL with the Adaptive Spatio-Temporal Attention-based Multi-Model (ASTAM) to effectively model complex and non-linear spatio-temporal traffic correlations in a data-local FL setting. Within FedASTAM, the road network is divided into sub-regions using spectral clustering, allowing each sub-region to train a local ASTAM model tailored to localized and heterogeneous traffic patterns. At the central server, locally trained models are aggregated using seven aggregation schemes, including the classical FedAvg, to optimize global model updates while preserving data locality. Extensive experiments conducted on two real-world benchmark datasets, PeMS04 and PeMS08, demonstrate that FedASTAM achieved strong and stable predictive performance while keeping raw data localized throughout the federated training process. The results further indicate that the aggregation approaches used in the proposed FedASTAM framework generally outperform classical FedAvg under heterogeneous traffic conditions, highlighting FedASTAM as an effective approach for traffic flow prediction in complex, distributed ITS environments. Full article