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

2023-06-04

IEEE International Conference on Acoustics, Speech, and Signal Processing

AI Summary

This work addresses the detrimental impact of communication noise on training stability and accuracy in SplitFed learning for medical image segmentation. To mitigate this issue, the authors propose an intelligent averaging strategy that enhances model robustness against highly noisy communication channels within the Split-Federated learning framework. The proposed method maintains high segmentation accuracy while tolerating communication noise levels up to two orders of magnitude greater than those manageable by conventional averaging mechanisms. Consequently, it significantly improves both the stability and performance of the system under adverse channel conditions, enabling more reliable deployment of SplitFed learning in real-world medical imaging applications where communication quality may be limited.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Agent Communication

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Split-Federated learning
• channel noise
• embryo image segmentation
• communication reliability
• federated learning

Innovation

• Split-Federated Learning
• Smart Averaging
• Noisy Channels
• Embryo Image Segmentation
• Communication Robustness