  Federated learning (FL) enables massive distributed Information and
Communication Technology (ICT) devices to learn a global consensus model
without any participants revealing their own data to the central server.
However, the practicality, communication expense and non-independent and
identical distribution (Non-IID) data challenges in FL still need to be
concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which
differs from the FL in two aspects, local clients clustering and in-cluster
training. A sequential training manner is designed for our in-cluster training
in this paper which enables the neighboring clients to share their learning
models. The proposed Semi-FL can be easily applied to future mobile
communication networks and require less up-link transmission bandwidth.
Numerical experiments validate the feasibility, learning performance and the
robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the
existing potentials of FL.
