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PackVFL: Efficient HE Packing for Vertical Federated Learning
May 2, 2024, 4:11 a.m. | Liu Yang, Shuowei Cai, Di Chai, Junxue Zhang, Han Tian, Yilun Jin, Kun Guo, Kai Chen, Qiang Yang
cs.CR updates on arXiv.org arxiv.org
Abstract: As an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to data inflation and time-consuming operations. To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms. PackVFL packs multiple cleartexts into one ciphertext and supports single-instruction-multiple-data (SIMD)-style parallelism. We focus on designing a high-performant matrix multiplication (MatMult) method since …
accelerate arxiv consuming cs.cr cs.lg data distributed efficiency encryption federated federated learning framework homomorphic encryption inflation machine machine learning operations problems tool
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