QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

arXiv:2606.09869v1 Announce Type: new Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, meaning the layer where the model is divided still remains a critical challenge, especially when clients have heterogeneous hardware capabilities. Fixed split points can overload weak devices and increase the communication and server load, which slows convergence and reduces stability. This pap

Source: arXiv cs.LG — read the full report at the original publisher.

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