When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation
\textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients. However, under client heterogeneity, the conventional static split strategy may be suboptimal because clients can differ in data distributions, adaptation dynamics, and representation learning progress, making a single split point insufficient to accommodate client-specific training states. In this paper, we propose \textsc{FedSGA}, a \textbf{S}ufficiency-\textbf{G}uided \textbf{A}daptive split \textbf{Fed}erated learning framework that addresses this question through client-specific shallow sufficiency estimation. First, we introduce a client-specific adaptatio...
| Authors | Wenhao Yuan, Chenchen Lin, Wentao Hu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 16 August 2026 |
| Last updated at source | 20 August 2026 |
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