Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning

作者：Chong Yu, Shuaiqi Shen, Shiqiang Wang, Kuan Zhang, Zhao Hai · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2024 · DOI：10.1109/tnnls.2024.3383748 · 被引用次数：15 · 研究领域：Privacy-Preserving Technologies in Data、Vehicular Ad Hoc Networks (VANETs)、Stochastic Gradient Optimization Techniques

Electronic healthcare (e-health) allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by artificial intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. First, medical data are both horizontally and vertically partitioned. Since single horizontal federated learning (HFL) or vertical federated learning (VFL) techniques cannot deal with both types of data partitioning, directly applying them may consume excessive communication cost due to transmitting a part of raw data when requiring high modeling accuracy. Second, a naive combination of HFL and VFL has limitations including low training efficiency, unsound convergence analysis, and lack of parameter tuning strategies. In this article, we provide a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data are both horizontally and vertically partitioned. Specifically, we propose a hybrid federated learning framework with one intermediate result exchange and two aggregation phases. Based on this framework, we develop a hybrid stochastic gradient descent (HSGD) algorithm to train models. Then, we theoretically analyze the convergence upper bound of the ...