HOSL reduces client memory up to 3.7x versus full first-order split learning while staying within 0.20-4.23% accuracy on OPT models by pairing client zeroth-order estimation with server first-order optimization.
For zeroth-order optimiza- tion, we fixϵ= 1e-3and use 10 perturbation vectors across all configurations
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HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
HOSL reduces client memory up to 3.7x versus full first-order split learning while staying within 0.20-4.23% accuracy on OPT models by pairing client zeroth-order estimation with server first-order optimization.