federated learning

Securing Supply Chain Data Integrity

The digitization of global supply chains has enhanced efficiency but simultaneously expanded the attack surface for cyber-sabotage and insider manipulation. Ensuring data integrity across interconnected entities has become a strategic necessity for operational resilience. This chapter presents an AI-driven multi-layered framework integrating machine learning, deep learning, federated learning, and explainable AI to strengthen data authenticity and transparency. The framework detects behavioral and contextual anomalies, preserves privacy through decentralized intelligence, and supports auditability via interpretable decision outputs. Analytical case studies from pharmaceutical and semiconductor domains demonstrate adaptability across heterogeneous supply environments. The study further outlines future research directions involving quantum-safe architectures, sustainable AI, and ethical governance, positioning artificial intelligence as a cornerstone for secure, trustworthy, and resilient supply chain ecosystems.

Decentralized Federated Learning with Differential Privacy for Sensor Networks

Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are susceptible to attacks, particularly in the decentralized nature of sensor networks. This paper proposes a novel DFL framework that integrates differential privacy mechanisms at each sensor node alongside a Byzantine fault tolerance protocol. This combination ensures both privacy preservation and model integrity, addressing critical vulnerabilities inherent in existing decentralized learning systems. The framework utilizes a distributed aggregation strategy, minimizing communication overhead and enhancing resilience against malicious actors. The key contribution lies in the synergistic combination of differential privacy and Byzantine fault tolerance, providing a practical and secure solution for training machine learning models in decentralized sensor networks. The theoretical analysis demonstrates the privacy budget consumption and the effectiveness of the proposed protocol in mitigating the impact of Byzantine attacks. The system design prioritizes scalability and adaptability, crucial factors for deployment in diverse sensor network scenarios. This work provides a foundational approach for secure and efficient DFL in sensor networks, paving the way for innovative applications in areas such as environmental monitoring, smart cities, and industrial IoT.

Predictive Maintenance using Federated Learning with Edge-Based Anomaly Detection

Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel framework that leverages the strengths of federated learning and edge-based anomaly detection to achieve robust and privacy-preserving predictive maintenance. The core idea is to train a predictive maintenance model collaboratively across multiple devices using federated learning, while simultaneously employing local anomaly detection models residing on edge devices to identify deviations from normal operation *before* data transmission. This approach reduces the amount of raw data sent to a central server, thereby mitigating privacy risks and potentially improving model accuracy through localized anomaly detection. The framework is designed to be scalable and adaptable to various industrial environments. We present a detailed discussion of the system architecture, the federated learning process, and the edge-based anomaly detection techniques. The proposed method offers a compelling solution for industries seeking to proactively manage equipment health while safeguarding sensitive data.

Development Of a Federated Learning Model for Fair Healthcare Data Analysis in Selected African Countries

Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in low-resource African settings, where intermittent connectivity, client dropout, limited computational capacity, and heterogeneous data may affect model performance. Demographic and Health Survey microdata from Nigeria (2024, n = 13,594) and Kenya (2022, n = 10,380), comprising 23,974 observations and 15 predictor features after pre-processing, were used to develop a horizontal FL simulation. Data was partitioned across four simulated municipal clients according to country and urban-rural strata to represent heterogeneous, non-identically distributed conditions. Federated Averaging (FedAvg) and coordinate-wise median aggregation were evaluated under clean conditions and three injected failure conditions such as client dropout, stale updates, and Byzantine corruption, while non-IID characteristics were represented through the client partitioning strategy. Logistic Regression and XGBoost served as centralised baselines. Model explanations were assessed using SHAP, with explanation stability measured using Top-K Jaccard similarity and Kendall’s tau rank correlation, while fairness was evaluated using demographic parity difference and equal opportunity difference across wealth quintiles and urban-rural strata. XGBoost achieved the strongest centralised performance (AUC = 0.7449; F1 = 0.7419). FedAvg achieved a slightly higher AUC of 0.7549 under clean federated conditions. Moreover, Byzantine corruption reduced FedAvg performance to near-random prediction (AUC = 0.4988), whereas coordinate-wise median maintained an AUC of 0.7116. Stale updates produced the greatest fairness degradation, while coordinate-wise median generally demonstrated lower wealth-based disparities than FedAvg. Explanation stability was also limited across all conditions, with Top-K Jaccard similarity below 0.82 and Kendall’s tau below 0.47 relative to the centralised benchmark. Education level and wealth quintile consistently emerged as the strongest predictors of ANC utilisation. The findings demonstrate that FL is viable for privacy-preserving maternal health prediction but requires robust aggregation, stale-update mitigation, and joint assessment of fairness and explanation stability. The observed socioeconomic disparities also highlight the need for complementary policy interventions beyond model design.

Privacy-preserving intrusion detection in IoT smart homes using a federated hybrid 1D-CNN–LSTM model with explainable AI

Abstract The proliferation of Internet of Things (IoT) devices in smart home environments has dramatically expanded the attack surface for cyber threats, particularly botnet-driven Distributed Denial of Service (DDoS) attacks. Centralized Intrusion Detection Systems (IDS) are ill-suited to this domain because they violate user privacy, introduce single points of failure, and incur prohibitive communication overhead. Federated Learning (FL) offers a compelling privacy-preserving alternative, yet existing FL-based IDS solutions either deploy convolutional or recurrent models in isolation, lack human-interpretable outputs, or neglect real-world deployability constraints. This paper proposes FedShield-IDS , a novel federated intrusion detection framework that integrates a hybrid one-dimensional Convolutional Neural Network with Long Short-Term Memory units to simultaneously capture spatial traffic fingerprints and long-range temporal attack dynamics across IoT edge devices. Model interpretability is addressed through the integration of SHapley Additive exPlanations (SHAP), enabling administrators to receive human-readable justifications for every detected anomaly. The system is trained and evaluated on the large-scale CICIoT2023 dataset, comprising 712,311 flow records spanning eight attack families including DDoS, DoS, Mirai, Reconnaissance, Spoofing, Injection, and Malware. A multi-stage preprocessing pipeline combining infinite-value imputation, logarithmic feature scaling, Min-Max normalization, temporal windowing, and localized SMOTE oversampling is applied within each federated client to address non-IID data and extreme class imbalance. Federated Averaging aggregates encrypted model updates across seven virtual IoT client nodes over five communication rounds without exchanging raw traffic data, under a formal threat model characterizing the system’s adversarial assumptions and data-confidentiality guarantees. Experimental results demonstrate a Mirai F1-score of 0.99, a DDoS precision of 0.97, and a global weighted F1-score of 0.76 across all eight classes. Comprehensive kernel-size, architecture, and preprocessing ablations confirm the necessity of each design choice, and independent cross-dataset evaluation on the Edge-IIoTset benchmark achieves 98.58% accuracy, demonstrating strong generalization beyond CICIoT2023. The framework achieves sub-500 ms threat mitigation, empirically confirmed via a mitigation-gate threshold sensitivity analysis, and generates SHAP-gated explanations for every alert, bridging the gap between high-accuracy detection and the transparency required for trustworthy smart-home security.

Development Of a Federated Learning Model for Fair Healthcare Data Analysis in Selected African Countries

Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in low-resource African settings, where intermittent connectivity, client dropout, limited computational capacity, and heterogeneous data may affect model performance. Demographic and Health Survey microdata from Nigeria (2024, n = 13,594) and Kenya (2022, n = 10,380), comprising 23,974 observations and 15 predictor features after pre-processing, were used to develop a horizontal FL simulation. Data was partitioned across four simulated municipal clients according to country and urban-rural strata to represent heterogeneous, non-identically distributed conditions. Federated Averaging (FedAvg) and coordinate-wise median aggregation were evaluated under clean conditions and three injected failure conditions such as client dropout, stale updates, and Byzantine corruption, while non-IID characteristics were represented through the client partitioning strategy. Logistic Regression and XGBoost served as centralised baselines. Model explanations were assessed using SHAP, with explanation stability measured using Top-K Jaccard similarity and Kendall’s tau rank correlation, while fairness was evaluated using demographic parity difference and equal opportunity difference across wealth quintiles and urban-rural strata. XGBoost achieved the strongest centralised performance (AUC = 0.7449; F1 = 0.7419). FedAvg achieved a slightly higher AUC of 0.7549 under clean federated conditions. Moreover, Byzantine corruption reduced FedAvg performance to near-random prediction (AUC = 0.4988), whereas coordinate-wise median maintained an AUC of 0.7116. Stale updates produced the greatest fairness degradation, while coordinate-wise median generally demonstrated lower wealth-based disparities than FedAvg. Explanation stability was also limited across all conditions, with Top-K Jaccard similarity below 0.82 and Kendall’s tau below 0.47 relative to the centralised benchmark. Education level and wealth quintile consistently emerged as the strongest predictors of ANC utilisation. The findings demonstrate that FL is viable for privacy-preserving maternal health prediction but requires robust aggregation, stale-update mitigation, and joint assessment of fairness and explanation stability. The observed socioeconomic disparities also highlight the need for complementary policy interventions beyond model design.

Decentralized Federated Reinforcement Learning with Byzantine Agreement

Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning process and potentially leading to catastrophic outcomes. This paper introduces a novel framework for DFRL that incorporates Byzantine agreement protocols to mitigate these vulnerabilities. Our approach enables agents to collaboratively learn while simultaneously resisting manipulation and ensuring convergence. We formalize the problem, define the key components of the system, and present a theoretical analysis demonstrating the effectiveness of our method. The core claim is that training RL agents across multiple devices introduces vulnerabilities to malicious actors. The core mechanism applies Byzantine agreement protocols to decentralized federated RL, enabling agents to learn collaboratively while resisting manipulation and ensuring convergence. This work significantly advances the field by providing a resilient and trustworthy solution for DFRL, opening up new possibilities for real-world deployments. ---

Training Without Gathering the Data: A State of the Art Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Distributed Learning with Federated Generative Adversarial Networks

This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. This research addresses these limitations by enabling collaborative GAN training across multiple decentralized devices, termed as Federated Generative Adversarial Networks (FedGANs). The core claim is that training GANs across distributed devices presents substantial challenges, and this paper proposes a framework to overcome these challenges. The framework leverages federated learning to allow devices to generate data collaboratively without sharing raw data, thereby preserving data privacy. The design focuses on maintaining the integrity and diversity of generated samples. This work introduces a new approach to federated learning specifically optimized for GANs, contributing to the advancement of privacy-preserving and decentralized generative modeling. The proposed system is evaluated conceptually, outlining the key components and potential improvements.

Training Without Gathering the Data: A Comparative Analysis Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Decentralized Federated Learning with Differential Privacy for Sensor Networks

Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are susceptible to attacks, particularly in the decentralized nature of sensor networks. This paper proposes a novel DFL framework that integrates differential privacy mechanisms at each sensor node alongside a Byzantine fault tolerance protocol. This combination ensures both privacy preservation and model integrity, addressing critical vulnerabilities inherent in existing decentralized learning systems. The framework utilizes a distributed aggregation strategy, minimizing communication overhead and enhancing resilience against malicious actors. The key contribution lies in the synergistic combination of differential privacy and Byzantine fault tolerance, providing a practical and secure solution for training machine learning models in decentralized sensor networks. The theoretical analysis demonstrates the privacy budget consumption and the effectiveness of the proposed protocol in mitigating the impact of Byzantine attacks. The system design prioritizes scalability and adaptability, crucial factors for deployment in diverse sensor network scenarios. This work provides a foundational approach for secure and efficient DFL in sensor networks, paving the way for innovative applications in areas such as environmental monitoring, smart cities, and industrial IoT.

CSAFL: Communication-Efficient and Staleness-Aware Asynchronous Federated Learning for LEO Satellite Networks