Split Learning With Local Epoch Regulation and Time-Aware Detection

Summary

Researchers propose CoDefend, a split learning framework for edge computing that combines local epoch regulation with time-aware detection. The method assigns heterogeneous clients appropriate local epoch numbers and uses a detection window to identify malicious manipulation. Experiments on simulated and NVIDIA Jetson edge platforms show roughly 2× faster training than baselines, with comparable accuracy and effective detection even under collusion.

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