privacy-preserving machine learning

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• Published Ahead of print Subscription Review Article

  Enhancing Data Security and Privacy in Machine Learning Using Federated

  By Krishna Kant Jha, Aayush Bisht, Arun Kumar Rawat, Ajay Vishwakarma, Devesh Tiwari

  Abstract: Federated learning is a way of training machine learning models. It does this across devices or organizations without putting all the raw data in one place. This helps to keep data secure in modern artificial intelligence systems. This paper looks at how federated learning helps to keep data from being seen reduces the chance of something going wrong and lets people keep control over information. This makes the whole security …

  Published in Journal Of Network security Read article →

• Published Subscription Review Article 20 Mar 2026

  Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures

  By Pushpendra Kumar Sikarwal, Mukesh Kumar Gupta, Adamya Gupta

  Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and graphics processing unit (GPU)-based architectures without centralizing sensitive data. This work proposes a parallel adaptive federated learning (AFL) framework that integrates differential privacy and secure aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, …

  Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 09–16 Read article →