A Communication-efficient Federated learning assisted by Central data: Implementation of vertical training into Horizontal Federated learning

Federated learning (FL) has emerged to jointly train a model with distributed data sets in IoT while avoiding the need for central data collection. Due to limited observation range, such data sets can only reflect local information, which limits the quality of trained models. In practical network, the global information and local observations always coexist, which requires joint consideration for learning to make reasonable policy. However, in horizontal FL among distributed clients, the central agency only acts as a model aggregator without utilizing its global features to further improve the model. This could largely degrade the performance in some missions such as flow prediction, where the global information could obviously enhance the accuracy. Meanwhile, such global feature may not be directly transmitted to agents for data security. Then how to utilize the global observation residing in the central agency while protecting its safety rises up as an important problem in FL. In this paper, we developed the vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to vertical FL without extra communication rounds. Considering the delay and packet loss, we analyzed its convergence in the network system and validated its performance by experiments. The proposed VHFL could enhance the accuracy compared with the horizontal FL while protecting the security of global data.