Mapping Emulation for Knowledge Distillation
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This paper formalizes the source-blind knowledge distillation problem that is essential to federated learning. A new geometric perspective is presented to view such a problem as aligning generated distributions between the teacher and student. With its guidance, a new architecture MEKD is proposed to emulate the inverse mapping through generative adversarial training. Unlike mimicking logits and aligning logit distributions, reconstructing the mapping from classifier-logits has a geometric intuition of decreasing empirical distances, and theoretical guarantees using the universal function approximation and optimal mass transportation theories. A new algorithm is also proposed to train the student model that reaches the teacher's performance source-blindly. On various benchmarks, MEKD outperforms existing source-blind KD methods, explainable with ablation studies and visualized results.
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