Reinforcement Learning Framework Accelerates Fully Homomorphic Encryption Code

Global: Reinforcement Learning Framework Accelerates Fully Homomorphic Encryption Code

A new framework called CHEHAB RL leverages deep reinforcement learning to automate the optimization of fully homomorphic encryption (FHE) programs, delivering execution speeds 5.3 times faster and noise growth 2.54 times lower than the leading Coyote compiler, while cutting compilation time by a factor of 27.9.

Background on Fully Homomorphic Encryption

FHE enables computation on encrypted data without decryption, a capability that underpins privacy‑preserving cloud services and secure multiparty computation. Despite its promise, the technique remains hampered by high computational overhead, which has limited widespread adoption.

Reinforcement Learning Approach

The CHEHAB RL system replaces hand‑crafted heuristics with an RL agent trained to select sequences of rewriting rules that vectorize scalar FHE code. To generate a diverse training set, the researchers synthesized workloads using a large language model, ensuring coverage of both structured and unstructured code patterns.

Compiler Integration

After training, the policy was embedded into the CHEHAB FHE compiler. The integrated solution automatically applies the learned transformations during compilation, eliminating the need for expert‑level cryptographic tuning.

Performance Evaluation

The authors benchmarked the system against Coyote, a state‑of‑the‑art vectorizing FHE compiler, using a suite of representative workloads. All tests measured execution latency, noise accumulation, and compilation duration.

Results and Implications

Across the benchmark set, CHEHAB RL achieved a geometric‑mean execution speedup of 5.3×, reduced noise growth by 2.54×, and accelerated the compilation process by 27.9× compared with Coyote. These gains suggest that reinforcement‑learning‑driven optimization could lower the barrier to practical FHE deployment, potentially expanding its use in secure data analytics and cloud computing.

This report is based on information from arXiv, licensed under See original source. Source attribution required.