arXiv Machine Learning August 20, 2026 By Idan Roth, Lutz Lampe

GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks

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The paper introduces GQ-FSL, a green quantized federated split learning framework designed for wireless edge networks. It uses stochastic quantization for both local training and wireless transmissions, allowing asymmetric precision between client and server submodels to balance device energy limits with global convergence. The authors develop energy models and a convergence bound for heterogeneous data, then formulate an optimization problem to set the DNN split point and precision levels, achieving lower energy consumption while meeting latency and accuracy targets.

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