Jul 2026· International Conference Computing Methodologies and Communication· pp. 1266-1272· 0 citations· 22 references
Abstract
The growing complexity of cyber threats requires the creation of predictive models that are capable of integrating knowledge based on decentralized datasets without information disclosure. The traditional centralized methodology is hindered by strict privacy laws and the cyber security risks that they bring. We answer this question by proposing an AI-based Federated Learning (AIFL) framework which is purpose-built and optimized to ensure the confidential prediction of cyber security incidents, and which is optimally configured as a multi-tier hierarchical structure, synergizing differential privacy, secure multi-party computation (SMPC), and homomorphic encryption. AIFL promotes the joint model training without revealing the local data. As an additional iteration of this process, we weaken Privacy-Aware Weighted Federated Averaging (PAW-Fed Average), which allocates weights to the contribution of clients according to data integrity and the amount of privacy loss measurably caused. Empirical analyses of the CIC-IDS2017 and TON-IOT baseline datasets reveal that AIFL can achieve predictive performance within 2.1% of the centralized variants, enhance data privacy surpassing more than 95%, and also optimize communication overheads, by about 30 percent, as compared to the conventional Fed Avg, and creates a viable framework to realize secure, collaborative cyber-defense.
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity risks by exposing distributed connected devices to increasingly complex and pervasive threats. Conventional centralized security mechanisms often struggle to accommodate the heterogeneous and decentralized structure of IoT networks. This study investigates Federated Learning (FL) as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead. A novel FL-based Intrusion Detection System (IDS) architecture was developed using Convolutional Neural Networks (CNNs) for anomaly detection and the Federated Averaging (FedAvg) algorithm for aggregating local model updates. The framework was evaluated on standard IoT datasets under non-independent and identically distributed (non-IID) data conditions to simulate heterogeneous real-world environments. Experimental results demonstrate that the proposed system achieved a detection accuracy of 94.6%, an F1-score of 93.8%, and a recall of 92.7%, outperforming centralized and standalone local learning methods. The framework also reduced communication overhead by 35% and achieved convergence 28% faster than conventional approaches. These findings demonstrate that FL can provide a scalable, privacy-preserving, and computationally efficient foundation for strengthening IoT cybersecurity. This study contributes a decentralized machine-learning architecture for real-time, adaptive, and privacy-conscious intrusion detection in large-scale IoT environments.
Mohammed Ajuji, Y. M. Malgwi, A. Ahmadu et al.· International Journal of Edu...· 0 citations
Traditional federated learning has challenges such as slow model convergence, lagging situational awareness, and risk of gradient privacy leakage in cyber threat collaborative analysis capability. This study proposes a federated learning privacy-preserving collaboration model based on threat intelligence drive and hierarchical aggregation. The model optimizes participating nodes through a dynamic client selection mechanism, uses a hierarchical aggregation strategy to balance the learning of basic features and advanced threat patterns, and introduces an adaptive differential privacy mechanism to strengthen gradient protection. The experiment is based on the CIC-IDS-2018 public dataset, which covers various types of attacks in real network environments, with a data volume of approximately 5 million pieces. It is divided into 50 clients in a non-independent and identically distributed manner to simulate cross organizational collaboration scenarios. In the threat detection task, the model designed by the research institute achieved an accuracy of 91.8% and an F1 Score of 89.8% compared to baseline models such as FedAvg, FedProx, and DP FedAvg. All indicators were superior to the comparison model. In addition, in the advanced persistent threat attack scenario, the attack chain detection rate increased to 95.6%, and the average detection time was shortened to 2.8 hours. In terms of privacy protection, the Rényi privacy loss was only 2.89 with a budget of ε=3.0. The proposed model effectively improves the efficiency and timeliness of collaborative detection of cross-organizational threats while ensuring data privacy and provides a feasible solution for building a safe and reliable collaborative defense system. It should be pointed out that while improving detection performance, the model introduces additional communication overhead caused by hierarchical aggregation and dynamic selection mechanisms. The average communication traffic in the experiment was about 13.8 GB. As the number of clients expands to a larger scale, the computational load and scheduling complexity of the coordination layer will further increase. In addition, although the non-independent and identically distributed data partitioning used in the experiment can simulate real heterogeneous scenarios, the convergence efficiency of the model under extreme distributions still needs further verification. The above limitations will be optimized in future work by introducing asynchronous aggregation and lightweight communication protocols.
The use of cloud-based web systems has increased the issue of data privacy, compliance on regulations and secure control of distributed enterprise information. The traditional centralized machine learning models mandate aggregation of data in one server which makes it more dangerous to expose sensitive information. To overcome these limitations, model combines TF-IDF-based features extractions with a deep neural network classifier trained under a federated learning system, and no longer needs raw this paper suggests a Hybrid Federated Privacy-Aware Deep Neural Network (HFPA-DNN) to learn the features of privacy-controllable data on the cloud. The data sharing among distributed cloud nodes is proposed. The model uses the Adam optimization algorithm, binary cross-entropy loss and federated averaging (FedAvg) to aggregate global models. A non-compulsory differentiation privacy system is involved in order to increase resistance to inference and reconstruction attacks. The HFPA-DNN performance is compared to the traditional machine learning baselines, which are Logistic Regression, Support Vector Machines, Random Forest, and centralized deep neural networks. As experimental findings indicate, the suggested strategy can attain a better accuracy, F1-score, and ROC-AUC, and a significant decrease in the risks of privacy exposure and preserving in scalable training performance. Through training curves, confusion matrices, ROC and precision- recall analysis, ablation studies and statistical comparison, the strength and use of the framework in ensuring data governance in the cloud in a secure setting is always verified. The results demonstrate that HFPA-DNN is an efficient trade-off to guarantee predictive performance, low computational cost, and privacy in distributed web system.
Divya sai Jaladi, Ashok Mallempati, Dr. B. Jegajothi· 2026 4th International Confe...· 0 citations
This review underscores the potential of FL to become a foundational technology in next-generation cybersecurity systems, enabling scalable and privacy-preserving threat mitigation across distributed infrastructures.
The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.
Sheetal Bawane, Leeladhar Chourasiya, S. Jain et al.· International journal of com...· 0 citations
The proposed framework introduces several innovative features, such as federated learning with momentum-based optimization, adaptive differential privacy, trust verification via blockchain, and Byzantine-resilient aggregation, to enhance the security, scalability, and robustness of the system compared with traditional intrusion detection systems.
M. Ramzan· Journal of Communications· 0 citations
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