mlsanityData to 14 Oct 2026

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

arXiv 2607.29659 ↗First posted 31 Jul 2026cs.LG cs.DC eess.SPcited by 0

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Abstract (arXiv)

Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-device computational burdens by offloading workloads to an edge server, this may introduce systemic overheads, while the continuous exchange of intermediate activations, gradients, and submodels still incurs significant energy consumption (EC). To address this, we propose a green quantized FSL (GQ-FSL) framework that incorporates stochastic quantization for both local collaborative training and wireless transmissions. Notably, GQ-FSL supports asymmetric precision levels for the client- and server-side submodels, effectively decoupling device energy constraints from global convergence degradation. To quantify these tradeoffs, we develop parameterized energy models for the split architecture and derive a theoretical convergence bound under statistically heterogeneous data. Building on that, we formulate a joint optimization problem to configure the DNN split point and precision levels, minimizing the total system EC while satisfying strict latency and target accuracy constraints. Ultimately, we demonstrate that GQ-FSL enables large-scale DNN deployment on resource-constrained devices, achieving superior energy efficiency compared to quantized federated learning and full-precision FSL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 52 references that are in the map. First posted on arXiv 31 Jul 2026.

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References · 39

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27 more
  1. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  2. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  3. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
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