Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation

TL;DR

This paper investigates the impact of noisy communication channels on Split-Federated learning for embryo image segmentation and introduces a smart averaging strategy that significantly enhances noise resilience without sacrificing model accuracy.

Contribution

The paper proposes a novel smart averaging method for SplitFed learning that improves robustness against communication noise in federated learning systems.

Findings

Smart averaging tolerates 100 times more noise than conventional methods.

Final model accuracy remains stable despite increased channel noise.

Enhanced noise resilience without additional computational costs.

Abstract

Split-Federated (SplitFed) learning is an extension of federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of the overall model is deployed on the clients hardware. In SplitFed learning, feature values, gradient updates, and model updates are transferred across communication channels. In this paper, we study the effects of noise in the communication channels on the learning process and the quality of the final model. We propose a smart averaging strategy for SplitFed learning with the goal of improving resilience against channel noise. Experiments on a segmentation model for embryo images shows that the proposed smart averaging strategy is able to tolerate two orders of magnitude stronger noise in the communication channels compared to conventional averaging, while still maintaining the accuracy of the final model.