🤖 AI Summary
To address label distribution skew arising from data heterogeneity and partial client participation in Split Federated Learning (SFL), this paper proposes a novel collaborative logit calibration framework. The method introduces (1) a cross-client activation concatenation mechanism—first of its kind—to enable server-side unified modeling of the global label distribution; and (2) a dual-sided logit adjustment strategy operating jointly at the server and client levels, explicitly correcting class-wise bias in local loss functions—an innovation not previously realized in SFL. Theoretical analysis establishes enhanced convergence robustness under non-IID data assumptions. Empirical evaluation across multiple benchmark datasets demonstrates an average accuracy improvement of 3.2% and significantly accelerated convergence compared to state-of-the-art baselines.
📝 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 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.