Split Learning With Local Epoch Regulation and Time-Aware Detection

Split learning (SL, a technique that splits AI model training across multiple computers to reduce computational burden) faces efficiency and security problems in edge computing (distributed computing done on devices near data sources) environments, where slow computers can hinder training and malicious actors may sabotage the model. The paper proposes CoDefend, a framework that uses local epoch regulation (dynamically adjusting how many training rounds each computer performs) and time-aware detection (monitoring for suspicious behavior within specific time windows) to improve both training speed and security while protecting privacy.