HERL (Homomorphic Encryption Reinforcement Learning): A Reinforcement Learning-based Approach that Uses Q-Learning to Dynamically Optimize Encryption Parameters

Homomorphic Encryption Reinforcement Learning (HERL) offers practical solutions and value for enhancing privacy in Federated Learning (FL) scenarios, especially in sensitive industries like healthcare and finance. By integrating Homomorphic Encryption (HE), data privacy during training can be maintained, despite facing challenges such as computational and communication overheads in diverse client environments. HERL, a Reinforcement Learning-based technique, dynamically optimizes encryption parameters using Q-Learning to cater to different client groups. By focusing on optimizing coefficient modulus and polynomial modulus degree, HERL strikes a balance between security and computational load. In the implementation process, HERL profiles clients based on security needs and computing capabilities, clusters them into tiers, and dynamically selects encryption settings for each tier using Q-Learning. This approach enhances convergence efficiency, reduces convergence time, and improves utility without compromising security. Key contributions of HERL include adjusting encryption settings for dynamic FL, providing a better security, utility, and latency trade-off. It enhances FL operations’ efficiency while maintaining data security, showing up to a 24% increase in training efficiency. The research study explores the impact of HE parameters on FL performance, adapting to varied client environments, and optimizing security, computational overhead, and usefulness in FL with HE. It demonstrates the effectiveness of RL in dynamically adjusting encryption parameters for different client tiers. For more information, you can reach out to the AI Lab in Telegram @itinai for free consultation or follow them on Twitter @itinaicom.