  Federated Learning (FL) has emerged to allow multiple clients to
collaboratively train machine learning models on their private data at the
network edge. However, training and deploying large-scale models on
resource-constrained devices is challenging. Fortunately, Split Federated
Learning (SFL) offers a feasible solution by alleviating the computation and/or
communication burden on clients. However, existing SFL works often assume
sufficient labeled data on clients, which is usually impractical. Besides, data
non-IIDness poses another challenge to ensure efficient model training. To our
best knowledge, the above two issues have not been simultaneously addressed in
SFL. Herein, we propose a novel Semi-supervised SFL system, termed SemiSFL,
which incorporates clustering regularization to perform SFL with unlabeled and
non-IID client data. Moreover, our theoretical and experimental investigations
into model convergence reveal that the inconsistent training processes on
labeled and unlabeled data have an influence on the effectiveness of clustering
regularization. To mitigate the training inconsistency, we develop an algorithm
for dynamically adjusting the global updating frequency, so as to improve
training performance. Extensive experiments on benchmark models and datasets
show that our system provides a 3.8x speed-up in training time, reduces the
communication cost by about 70.3% while reaching the target accuracy, and
achieves up to 5.8% improvement in accuracy under non-IID scenarios compared to
the state-of-the-art baselines.
