Comprehensive Framework for Smart Hearing Ecosystem Using Federated Learning, Differential Privacy, and Secure Aggregation

Abstract

The increasing demand for intelligent hearing solutions in diverse auditory environments has highlighted the need for privacy-preserving, efficient, and adaptable auditory data processing. Current methods in smart hearing ecosystems suffer from critical limitations, including poor generalization on non IID data, privacy vulnerabilities, and inefficiencies in handling real-world variations. To address these challenges, we propose a comprehensive framework leveraging Federated Learning (FL) augmented with cutting-edge techniques, which ensures both privacy and performance. The proposed framework combines six key methodologies: Federated Averaging (FedAvg) with Adaptive Personalization for tailoring global models to user-specific auditory needs, Differentially Private Federated Learning (DP-FL) with Rényi Differential Privacy for robust privacy guarantees (ε = 2), Secure Aggregation using homomorphic encryption to remove data exposure risks, FedProx for stability across heterogeneous data distributions, Context-Aware Aggregation to favor high-quality auditory data, and Lightweight Edge AI Models for efficient, on-device feature extraction. These methods collectively assure superior accuracy, privacy, and efficiency in the analysis of auditory data. Experimental results show the effectiveness of the framework: it achieves classification accuracy of 90-92%, has privacy with minimal utility loss (<3%), secure aggregation latency of 1-2 seconds, and inference latency under 100ms. Moreover, it enhances convergence by 15-20% as compared with baseline methods. This work significantly advances the usability and effectiveness of smart hearing systems by bringing robust performance in a range of auditory contexts while providing strict privacy guarantees.