PackVFL: Efficient HE Packing for Vertical Federated Learning
CoRR(2024)
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 it
takes up most of the ciphertext computation time in HE-based VFL. Besides,
devising the MatMult method is also challenging for PackedHE because a slight
difference in the packing way could predominantly affect its computation and
communication costs. Without domain-specific design, directly applying SOTA
MatMult methods is hard to achieve optimal.
Therefore, we make a three-fold design: 1) we systematically explore the
current design space of MatMult and quantify the complexity of existing
approaches to provide guidance; 2) we propose a hybrid MatMult method according
to the unique characteristics of VFL; 3) we adaptively apply our hybrid method
in representative VFL algorithms, leveraging distinctive algorithmic properties
to further improve efficiency. As the batch size, feature dimension and model
size of VFL scale up to large sizes, PackVFL consistently delivers enhanced
performance. Empirically, PackVFL propels existing VFL algorithms to new
heights, achieving up to a 51.52X end-to-end speedup. This represents a
substantial 34.51X greater speedup compared to the direct application of SOTA
MatMult methods.
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