Federated Learning with Differential Privacy for Sensitive Domains

Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models across decentralized data sources while preserving data privacy. This approach is particularly beneficial in sensitive domains such as healthcare, finance, and telecommunications, where data privacy and regulatory compliance are paramount. This paper explores the integration of Federated Learning with Differential Privacy (DP) to enhance privacy guarantees during the training process. By allowing multiple entities to collaboratively train models without sharing raw data, FL mitigates the risks associated with centralized data storage. We detail the theoretical foundations of both Federated Learning and Differential Privacy, highlighting their complementary strengths in safeguarding sensitive information. Our empirical evaluations demonstrate the effectiveness of this integrated approach, showing that it can maintain model accuracy while significantly reducing the risk of privacy breaches. We present case studies in healthcare and financial services, illustrating how Federated Learning with Differential Privacy can be applied to real-world scenarios, ensuring compliance with regulations like HIPAA and GDPR. Furthermore, we discuss the trade-offs involved in implementing these techniques, including the impact on model performance and computational efficiency. The findings underscore the potential of Federated Learning combined with Differential Privacy as a robust framework for developing privacy-preserving machine learning solutions in sensitive domains. This research contributes to the ongoing discourse on ethical AI deployment, providing a pathway for leveraging advanced analytics while prioritizing user privacy and data security.