Communication-Efficient Split Learning via Adaptive Feature-Wise Compression

TL;DR

This paper introduces SplitFC, a split learning framework that significantly reduces communication costs through adaptive feature-wise dropout and quantization, maintaining high accuracy across multiple datasets.

Contribution

SplitFC is a novel framework that employs adaptive feature-wise dropout and quantization to improve communication efficiency in split learning.

Findings

Reduces communication overhead compared to existing methods.

Maintains high accuracy on MNIST, CIFAR-100, and CelebA datasets.

Provides a closed-form solution for optimal quantization levels.

Abstract

This paper proposes a novel communication-efficient split learning (SL) framework, named SplitFC, which reduces the communication overhead required for transmitting intermediate feature and gradient vectors during the SL training process. The key idea of SplitFC is to leverage different dispersion degrees exhibited in the columns of the matrices. SplitFC incorporates two compression strategies: (i) adaptive feature-wise dropout and (ii) adaptive feature-wise quantization. In the first strategy, the intermediate feature vectors are dropped with adaptive dropout probabilities determined based on the standard deviation of these vectors. Then, by the chain rule, the intermediate gradient vectors associated with the dropped feature vectors are also dropped. In the second strategy, the non-dropped intermediate feature and gradient vectors are quantized using adaptive quantization levels determined based on the ranges of the vectors. To minimize the quantization error, the optimal quantization levels of this strategy are derived in a closed-form expression. Simulation results on the MNIST, CIFAR-100, and CelebA datasets demonstrate that SplitFC outperforms state-of-the-art SL frameworks by significantly reducing communication overheads while maintaining high accuracy.