Abstract:Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data and privacy-preserving neural inference in the cloud. Existing solutions focus on models with dense inputs (e.g., CNNs and MLPs). Recommendation models (e.g., DLRM) pose a different challenge: sparse categorical inputs require private lookups into large embedding tables, which must be implemented using FHE's restrictive operators. Naive lookups incur significant communication and memory costs; prior work proposes compressing embedding tables at the expense of introducing large server-side compute costs (i.e., indicator function) and revealing embedding-table structure. We present HE-LRM, a performance optimized solution for executing recommendation with FHE. First, we develop an embedding compression technique using client-side digit decomposition that achieves 56$\times$ speedup over the state-of-the-art. Next, we propose a multi-embedding packing strategy that enables ciphertext SIMD-parallel lookups across multiple tables. We integrate HE-LRM into the open-source Orion FHE framework to demonstrate end-to-end encrypted DLRM inference. We evaluate HE-LRM on UCI (health prediction) and Criteo (click prediction), achieving inference latencies of 24 seconds on UCI and 228 to 489 seconds, respectively, on a single-threaded CPU. Finally, we show how GPU and ASIC FHE acceleration can reduce end-to-end latencies to seconds and even sub-seconds. Our code can be found at this https URL.
From: Karthik Garimella [view email]
[v1]
Sun, 22 Jun 2025 19:40:04 UTC (703 KB)
[v2]
Mon, 10 Nov 2025 19:38:25 UTC (669 KB)
[v3]
Thu, 19 Feb 2026 20:23:04 UTC (985 KB)
[v4]
Sat, 18 Jul 2026 20:18:57 UTC (1,782 KB)
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