STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mobile Computing, Communications & Mobile Networks Original Research

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

  1. Pushpendra Kumar Sikarwal Department of Computer Science, Suresh Gyan Vihar University, Jaipur
  2. Mukesh Kumar Gupta Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur
  3. Adamya Gupta Department of Computer Science and Engineering, Jaipur Engineering College and Research Centre, Jaipur

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 address these limitations to ensure that dynamic optimization and hybrid privacy preservation can be achieved. The proposed framework implements adaptive client participation and weighted aggregation with reference to local resource availability and convergence measures, thereby improving the efficiency of communication and model stability. Furthermore, AFL-P is an algorithm that combines differential privacy (DP) and secure aggregation (SA) to provide a stringent assurance of information leakage and no performance costs on the learning process. The CIFAR-10 (image), UCI-human activity recognition (HAR) (sensor), and Google Speech Commands (audio) experimental results show that AFL-P outperforms other baseline algorithms (FedAvg, DP-FedAvg, and FedProx) by 6–8 percent, 20 percent communication overhead, and more than 50 percent privacy loss. The findings confirm AFL-P is a strong, efficient, and privacy-conscious training model of heterogeneous and resource-constrained setups.

Keywords

References (10)

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