  Split Federated Learning (SFL) is a distributed machine learning framework
which strategically divides the learning process between a server and clients
and collaboratively trains a shared model by aggregating local models updated
based on data from distributed clients. However, data heterogeneity and partial
client participation result in label distribution skew, which severely degrades
the learning performance. To address this issue, we propose SFL with
Concatenated Activations and Logit Adjustments (SCALA). Specifically, the
activations from the client-side models are concatenated as the input of the
server-side model so as to centrally adjust label distribution across different
clients, and logit adjustments of loss functions on both server-side and
client-side models are performed to deal with the label distribution variation
across different subsets of participating clients. Theoretical analysis and
experimental results verify the superiority of the proposed SCALA on public
datasets.
