In this paper, we propose feature-based federated transfer learning as a
novel approach to improve communication efficiency by reducing the uplink
payload by multiple orders of magnitude compared to that of existing approaches
in federated learning and federated transfer learning. Specifically, in the
proposed feature-based federated learning, we design the extracted features and
outputs to be uploaded instead of parameter updates. For this distributed
learning model, we determine the required payload and provide comparisons with
the existing schemes. Subsequently, we analyze the robustness of feature-based
federated transfer learning against packet loss, data insufficiency, and
quantization. Finally, we address privacy considerations by defining and
analyzing label privacy leakage and feature privacy leakage, and investigating
mitigating approaches. For all aforementioned analyses, we evaluate the
performance of the proposed learning scheme via experiments on an image
classification task and a natural language processing task to demonstrate its
effectiveness.

本文提出了基于特征的联邦迁移学习作为一种新方法，通过将上行负载减少数个数量级，改善通信效率，与现有的联邦学习和联邦迁移学习方法相比具有更好的效果。具体而言，在提出的基于特征的联邦学习中，我们设计了要上传的提取特征和输出，而不是参数更新。对于这个分布式学习模型，我们确定了所需的负载，并与现有方案进行了比较。随后，我们分析了基于特征的联邦迁移学习在数据包丢失、数据不足和量化方面的鲁棒性。最后，我们通过定义和分析标签隐私泄露和特征隐私泄露，并研究缓解方法来解决隐私问题。对于所有上述分析，我们通过在图像分类任务和自然语言处理任务上进行实验，评估了提出的学习方案的性能。