2 publications

• Published Subscription Original Research 24 Apr 2026

  An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets

  By Pushpendra Kumar Sikarwal, Mukesh Kumar Gupta, Adamya Gupta

  Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …

  Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 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 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 →