Efficient Implementation of Reinforcement Learning over Homomorphic Encryption

• URL: http://arxiv.org/abs/2504.09335v1
• Date: Sat, 12 Apr 2025 20:34:26 GMT
• Title: Efficient Implementation of Reinforcement Learning over Homomorphic Encryption
• Authors: Jihoon Suh, Takashi Tanaka,
• Abstract summary: We classify control policy synthesis into model-based, simulator-driven, and data-driven approaches.<n>We examine their implementation over fully homomorphic encryption (FHE) for privacy enhancements.<n>Our work suggests the potential for secure and efficient cloud-based reinforcement learning.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate encrypted control policy synthesis over the cloud. While encrypted control implementations have been studied previously, we focus on the less explored paradigm of privacy-preserving control synthesis, which can involve heavier computations ideal for cloud outsourcing. We classify control policy synthesis into model-based, simulator-driven, and data-driven approaches and examine their implementation over fully homomorphic encryption (FHE) for privacy enhancements. A key challenge arises from comparison operations (min or max) in standard reinforcement learning algorithms, which are difficult to execute over encrypted data. This observation motivates our focus on Relative-Entropy-regularized reinforcement learning (RL) problems, which simplifies encrypted evaluation of synthesis algorithms due to their comparison-free structures. We demonstrate how linearly solvable value iteration, path integral control, and Z-learning can be readily implemented over FHE. We conduct a case study of our approach through numerical simulations of encrypted Z-learning in a grid world environment using the CKKS encryption scheme, showing convergence with acceptable approximation error. Our work suggests the potential for secure and efficient cloud-based reinforcement learning.

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