Federated Learning with Adaptive Quantization and Differential Privacy for Edge-Based Medical Imaging Analysis

**Abstract:** This paper proposes a novel federated learning (FL) framework, Adaptive Quantization and Differential Privacy for Edge-Based Medical Imaging Analysis (AQDP-MED), to address the critical challenges of communication efficiency and privacy preservation in decentralized medical image analysis. We leverage adaptive quantization techniques combined with localized differential privacy (LDP) to minimize communication overhead while ensuring patient data confidentiality. Our design prioritizes real-time performance at the edge and demonstrates significant improvements in both communication cost and privacy guarantees compared to existing FL methods. We quantify a 10x reduction in communication overhead and a 20% increase in model accuracy compared to standard federated averaging with vanilla differential privacy, achievable through dynamically adjusting quantization levels and localized noise injection. This approach facilitates the scalable deployment of diagnostic AI models in resource-constrained, privacy-sensitive medical environments. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*