Advanced Privacy-Preserving Federated Learning in 6G Networks Using Differential Privacy and Homomorphic Encryption
Keywords:
6G Network, Homomorphic Encryption, Differential Privacy, Data Security, Global Accuracy.Abstract
In the age of 6G networks, ensuring robust privacy and security measures is essential. Given the widespread connectivity and a plethora of applications, the management of extensive data in the realm of 6G, facilitated by cutting-edge technologies, demands an elevated level of safeguarding. Essentially, strong privacy measures cultivate trust, encouraging broad adoption by instilling confidence among both users and organizations. In this landscape, prioritizing privacy and security is paramount to safeguarding sensitive information, maintaining integrity, and mitigating risks associated with the dynamic and interconnected nature of 6G networks. The research introduces an advanced federated learning approach tailored for 6G networks. Utilizing differential privacy during localized model training and homomorphic encryption for secure transmission, the central server orchestrates secure aggregating encrypted updates. This collaborative learning model progressively enhances global accuracy while preserving individual data privacy. Robust monitoring ensures regulatory compliance and dynamic improvements to privacy mechanisms signify the proactive evolution of this paradigm within the enigmatic realms of 6G networks, offering a significant advancement in both model precision and privacy standards.Top of Form
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