Federated Learning with Adaptive Gradient Compression and Dynamic Aggregation for Privacy-Preserving Small-Sample Data

Abstract

This paper proposes a federated learning (FL) framework that incorporates adaptive gradient compression and dynamic aggregation to address communication efficiency and data privacy issues in the context of FL with limited sample size and non-IID data distributions in edge devices and resource-scarce environments. This proposed framework incorporates dynamic gradient compression techniques that compress gradients based on their magnitude and variance to ensure high communication efficiency with minimal loss in model accuracy. Meanwhile, the proposed framework incorporates dynamic aggregation techniques that assign different weights to clients based on their reliability to ensure effective model convergence in heterogeneous and scarce data distributions. Data privacy in the proposed framework is ensured through secure aggregation and Differential Privacy (DP) techniques. Experimental results on various datasets, including LEAF, FEMNIST, Reddit, and Shakespeare, show that the proposed framework ensures communication efficiency of over 70%, preserves model accuracy with minimal loss at 1–2%, and achieves 30% faster convergence speed compared to traditional FL techniques. These results show that the proposed framework is applicable in real-world scenarios in mobile edge computing and IoT applications, where communication efficiency and data privacy are significant factors for model convergence and deployment. The combination of gradient compression and dynamic aggregation in FL with strong privacy guarantees makes this framework a powerful tool for model convergence in heterogeneous scenarios.