SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments

arXiv:2405.04875v1 Announce Type: new
Abstract: 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 …

abstract arxiv client cs.ai cs.lg data distributed federated learning framework however machine machine learning process scala server split trains type