FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation

Fanfei Meng, Lele Zhang, Yu Chen, Yuxin Wang

Keywords: Vertical federated learning, hybrid federated learning, federated transfer learning

TL;DR: An efficient approach to addressing existing complex vertical and hybrid federated learning paradigm with fixed time complexity.

Abstract: Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modeling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modeling vertical and hybrid DNN-based learning. The idea of our algorithm is characterized by higher inference accuracy, stronger privacy- preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature & subject space decentralized problems, shows 0.3% to 4.2% inference accuracy improvement with limited privacy revealing for datasets stored in local clients, and reduces 88.9 % time complexity over vertical baseline method.