GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks
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 fo...
| Authors | Idan Roth, Lutz Lampe |
|---|---|
| Primary category | cs.LG |
| Published | 31 July 2026 |
| Last updated at source | 19 August 2026 |
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