U-Shaped Split Federated Learning with Compact Features for Deep Learning-Based Image Coding

TL;DR

This paper introduces a new framework for image coding that reduces communication costs in distributed learning while preserving image quality.

Contribution

The novel compact-feature U-shaped split federated learning framework (CoF U-SFL) reduces communication overhead using feature entropy estimation.

Findings

CoF U-SFL reduces communication overhead by 104.6 times compared to existing methods.

The framework maintains low image distortion during reconstruction.

A feature entropy estimation network effectively compresses intermediate features for transmission.

Abstract

U-shaped Split Federated Learning (U-SFL) is a promising paradigm for distributed image coding, offering parallel training capabilities and privacy preservation while mitigating computational burdens on edge devices. However, the frequent bidirectional transmission of intermediate features between dual-split points incurs substantial communication overhead. To mitigate this issue, we propose a compact-feature U-shaped split federated learning framework (CoF U-SFL), which reduces communication overhead and improves training efficiency while maintaining low image distortion. We introduce a feature entropy estimation network to model the distribution of split-layer features, enabling effective compression during transmission. Furthermore, we formulate a joint optimization objective incorporating entropy constraints to guide the end-to-end training. Experimental results demonstrate that CoF U-SFL reduces communication overhead by 104.6 times while maintaining reconstruction performance.

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