  Federated learning (FL) was proposed to achieve collaborative machine
learning among various clients without uploading private data. However, due to
model aggregation strategies, existing frameworks require strict model
homogeneity, limiting the application in more complicated scenarios. Besides,
the communication cost of FL's model and gradient transmission is extremely
high. This paper proposes Loosely Coupled Federated Learning (LC-FL), a
framework using generative models as transmission media to achieve low
communication cost and heterogeneous federated learning. LC-FL can be applied
on scenarios where clients possess different kinds of machine learning models.
Experiments on real-world datasets covering different multiparty scenarios
demonstrate the effectiveness of our proposal.
