Paper 2026/1212

SecLoRA: Secure Aggregation of Low-Rank Matrix Products via Functional Encryption

Abstract

Federated fine-tuning of Large Language Models via Low Rank Adaptation (LoRA) faces a critical privacy-efficiency trade-off: low-rank factors can leak sensitive data, yet standard secure aggregation is restricted to linear operations. Existing solutions for aggregating matrix products (e.g., $\mathbf{B}_i \mathbf{A}_i$) either sacrifice exactness, depend on a trusted third party, or incur prohibitive costs at scale. We present SecLoRA, the first decentralized framework achieving exact aggregation of LoRA updates with linear communication complexity. The core of SecLoRA is a novel cryptographic primitive: Pairwise Composable Multi-Client Functional Encryption (PC-DMCFE). Unlike traditional functional encryption, which treats ciphertext recombination as an attack, the dual-encryption architecture of PC-DMCFE ($\mathsf{Enc}_A, \mathsf{Enc}_B$) is intentionally designed to harness this property. It allows any ciphertexts $\mathsf{ct}_A$ and $\mathsf{ct}_B$ to be arbitrarily paired and evaluated via a functional key to reveal their inner product. This unique property enables secure and decentralized aggregation of matrix products without losing LoRA's linear communication advantages. Furthermore, SecLoRA ensures round-isolated decryption to prevent temporal leakage without extra interaction. Evaluation shows that SecLoRA is practical for cross-silo deployments.