13 results for “Federated Learning”

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Research ArticleOpen accessPhrase in the titleFL-CARE: Federated Learning-Based Contention-Aware and Energy-Balanced Routing for Dense FANETsHalimjon Khujamatov, Elyanora Jolimbetova et al.In dense multi-drone FANET environments, routing efficiency is often reduced due to constant competition for a wireless channel, frequent changes in network topology, and uneven energy consumption between UAV nodes. These factors lead to a decrease in the packet delivery ratio, an increase in delays on the busiest or most unstable routes, and a reduction in the total network lifespan. … …Journal of Computer Science, Vol. 22, Issue 8 · 13 Aug 2026 · 1,172 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleReal MDS Codes for Error Correction: Construction and an Application on Federated Learning Pre Aggregation ValidationRavi Varma B., N. Suresh Babu, Santhosh Kumar K. P., Shailesh SivanReal number codes play a crucial role in correcting errors within communication systems that involve the transmission of analog signals or continuous data. Among these codes, Maximum Distance Separable (MDS) codes stand out as highly efficient for error correction. The present research paper is on the construction of a class of real number MDS codes for any desired dimension. The … …Journal of Computer Science, Vol. 21, Issue 12 · 27 Feb 2026 · 1,347 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleA Novel Approach Based on Federated Learning for the Identification of Man in the Middle Attacks in IOT Networks Using BlockchainSarumathi S, Juliet Johny, Minal Khandare et al.Federated Learning (FL) enables cooperative model training across dispersed edge devices while protecting data privacy and providing localized insights without the need for centralized data aggregation. In the Internet of Things (IoT), federated learning enables cooperative model training among dispersed edge devices while protecting data privacy and providing localized insights without the need for centralized data aggregation. Nevertheless, Federated Learning’s … …Journal of Computer Science, Vol. 22, Issue 2 · 23 Feb 2026 · 1,613 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titlePrivacy-Preserving Deep Federated Learning on the Edge Using Homomorphic Encryption and Secure Multiparty ComputationNoman Aasif Gudur, Mohamed El-Dosuky, Sherif KamelThe increasing volume of consumer data necessitates reliable edge devices for personalized user experiences. Federated Learning (FL) offers a state-of-the-art approach to decentralized machine learning by leveraging data distributed across multiple client devices. However, user data privacy remains vulnerable to corruption through feature heterogeneity and malicious attacks. While several privacy-preserving techniques have been previously implemented, they suffer from implementation constraints … …Journal of Computer Science, Vol. 21, Issue 11 · 21 Dec 2025 · 4,381 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleEnhancing the CIC IoT Dataset 2023 for Improved Attack Detection through GANs Augmentation and Federated LearningShahad Alahmari, Noura AleisaThe escalating frequency and sophistication of cyber-attacks on Internet of Things (IoT) devices present a pressing challenge to cybersecurity. With IoT device connections projected to exceed 42 billion by 2025, the vulnerability of these devices to cyber-attacks has never been more evident. This paper investigates the integration of Machine Learning (ML) and data augmentation, specifically Generative Adversarial Networks (GAN) and … …Journal of Computer Science, Vol. 21, Issue 7 · 18 Jul 2025 · 9,111 accessesView PDF Cite

Review ArticleOpen accessPhrase in the titleFederated Learning for Analysis of Medical Images: A SurveyMuhammad Imran Sharif, Mehwish Mehmood, Md Palash Uddin et al.Machine learning models trained in medical imaging can help in the early detection, diagnosis, and prognosis of the disease. However, it confronts two major obstacles: deep learning models require access to a substantial amount of imaging data, which is a hard constraint, and the patient data is private and sensitive, so it cannot be shared like 1 other imaging data … …Journal of Computer Science, Vol. 20, Issue 12 · 28 Oct 2024 · 11,824 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleFed-Deep CTRL (Light GBM)-Scalable, Interpretable, and Privacy-Preserving Federated Learning Framework for Wireless Intrusion Detection SystemSudaroli Vijayakumar, V. MuthumanikandanA learning-based intrusion detection system automates the understanding and reporting of network traffic information. The systems using neural networks and machine learning have demonstrated good detection accuracy, but the accuracy is entirely reliant on the type and volume of data. Additionally, there are issues with its scalability, privacy, efficiency, and interpretability. With the innovative fed-deep CTRL (light GBM) method, this … …Journal of Computer Science, Vol. 20, Issue 4 · 29 Feb 2024 · 5,321 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleSecuring Mobile Devices from Malware: A Faceoff Between Federated Learning and Deep Learning Models for Android Malware ClassificationNarayan Subramanian, Logesh Ravi, Mithin Jain Shaan et al.Amidst the escalating threats of android malware, urgency mounts to detect issues while safeguarding user privacy. Traditional machine learning and deep learning methods, dealt with scalability challenges and privacy compromises, finding a potential remedy in federated learning. This study introduces a groundbreaking federated learning-based methodology and compares federated learning with traditional deep learning techniques for Android malware classification, employing renowned … …Journal of Computer Science, Vol. 20, Issue 3 · 23 Jan 2024 · 6,284 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleFedCarepod: Federated Quantum Transfer Learning Approach in Screening Process for Early Detection of Liver AbnormalitiesK MalaThe liver is the most frequently injured intra-abdominal organ that leads to complications. Visual interpretation depends on the expertise, and this justifies the need for automatic accelerated assistive prediction system. Classical and Quantum machine learning approaches are combined to leverage their benefits, and the feasibility of designing a novel system is explored. This approach successfully ends up with health care … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,068 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsAn Efficient Cluster Head Selection in IoV-Enabled VANETs Using a Federated Diffusion Convolutional Recurrent Neural Networks FrameworkP Subhash, Madana Srinivas, A. Ranjith et al.The IoV enables the formation of VANETs that require a strong selection of CHs for effective communication within high-velocity changing traffic scenarios. Existing approaches often face issues related to scalability, privacy, and adaptability. To meet the aforementioned urgent needs, we design a decentralized scheme for intelligent CH selection based on Federated Learning with Diffusion Convolutional Recurrent Neural Networks (DCRNNs). According … …Journal of Computer Science, Vol. 22, Issue 10 · 1 Oct 2026 · 599 accessesView PDF Cite