Privacy-preserving machine learning

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• 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 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, and aggregation weights …

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