Federated Learning (FL) has emerged to allow multiple clients to
collaboratively train machine learning models on their private data. However,
training and deploying large models for broader applications is challenging in
resource-constrained environments. Fortunately, Split Federated Learning (SFL)
offers an excellent solution by alleviating the computation and communication
burden on the clients SFL often assumes labeled data for local training on
clients, however, it is not the case in practice.Prior works have adopted
semi-supervised techniques for leveraging unlabeled data in FL, but data
non-IIDness poses another challenge to ensure training efficiency. Herein, we
propose Pseudo-Clustering Semi-SFL, a novel system for training models in
scenarios where labeled data reside on the server. By introducing Clustering
Regularization, model performance under data non-IIDness can be improved.
Besides, our theoretical and experimental investigations into model convergence
reveal that the inconsistent training processes on labeled and unlabeled data
impact the effectiveness of clustering regularization. Upon this, we develop a
control algorithm for global updating frequency adaptation, which dynamically
adjusts the number of supervised training iterations to mitigate the training
inconsistency. Extensive experiments on benchmark models and datasets show that
our system provides a 3.3x speed-up in training time and reduces the
communication cost by about 80.1% while reaching the target accuracy, and
achieves up to 6.9% improvement in accuracy under non-IID scenarios compared to
the state-of-the-art.

在资源受限环境中，通过介绍聚类正则化和全局更新频率适应的控制算法，我们提出了一种新的训练模型系统，用于解决联合学习中的数据非独立同分布和模型收敛性问题，该系统在减少训练时间和通信成本的同时提高了准确性。