用学习式压缩减少分拆联邦学习的数据传输量。
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
- 在分拆联邦学习中引入可学习的压缩机制，降低通信开销。
- 医疗图像分割实验表明，数据传输量显著减少且模型精度保持不变。
- 适合对数据隐私和通信效率要求高的医疗等场景使用。
联邦学习（FL）使多个客户端能够在不共享本地数据的情况下协同训练模型。分拆学习（SL）允许模型在不同位置以分段方式训练。分拆联邦学习（SplitFed）结合了FL与SL的优势，通过在客户端和服务器间均衡计算负载，减轻了联邦学习的计算负担，同时保护数据隐私，特别适用于医疗等对隐私要求极高的领域。然而，SplitFed网络面临诸多通信挑战，如延迟、带宽限制、同步开销以及训练过程中需传输大量数据。本文提出SplitFedZip——一种新颖的方法，采用可学习压缩来减少分拆联邦学习中的数据传输。在医疗图像分割任务上的实验表明，该方法能显著降低通信量，同时维持最终模型的精度。代码已开源： https://github.com/ChamaniS/SplitFedZip。
原文摘要 · Abstract (English)
Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFed minimizes the computational burden of FL by balancing computation across clients and servers, while still preserving data privacy. This makes it an ideal learning framework across various domains, especially in healthcare, where data privacy is of utmost importance. However, SplitFed networks encounter numerous communication challenges, such as latency, bandwidth constraints, synchronization overhead, and a large amount of data that needs to be transferred during the learning process. In this paper, we propose SplitFedZip -- a novel method that employs learned compression to reduce data transfer in SplitFed learning. Through experiments on medical image segmentation, we show that learned compression can provide a significant data communication reduction in SplitFed learning, while maintaining the accuracy of the final trained model. The implementation is available at: \url{https://github.com/ChamaniS/SplitFedZip}.
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