1 publication

• 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 →