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Forbes article explains split learning, a technique that enables AI models to train on sensitive data without exposing raw data, improving privacy compliance and reducing communication costs.
QSplitFL proposes a DQN-based framework for optimal split point selection in split federated learning, using client hardware metrics to adapt to heterogeneous devices. Experiments show improved convergence and accuracy across multiple datasets and architectures.