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.

提出了一种名为 SCALA 的分布式机器学习框架，通过在服务器和客户端之间分离学习过程，并聚合基于分布式客户端数据更新的局部模型来共同训练共享模型。该框架解决了数据异质性和部分客户端参与导致的标签分布倾斜问题，并通过将客户端模型的激活连接起来以集中调整不同客户端之间的标签分布，并通过在服务器和客户端模型上执行的损失函数的 logit 调整来处理参与客户端的各个子集之间的标签分布变化。理论分析和实验证明了 SCALA 在公共数据集上的优越性。