基于特征投影与自适应增强的联邦蒸馏方法OA

To address the problem that existing federated distillation methods struggle to handle inconsistent representa-tion spaces and uneven feature distributions across heterogeneous models,a federated distillation method based on fea-ture projection and adaptive enhancement was proposed,achieving efficient knowledge fusion across heterogeneous cli-ent models.On the server side,client outputs were integrated via distillation.On the client side,lightweight multi-exit branches were employed to project intermediate features into an aligned logits space to overcome feature alignment bottlenecks.Promising performance was demonstrated on benchmark datasets,with reduced communication rounds and data transmission as well as enhanced system robustness,thus providing an effective new solution for heterogeneous model knowledge fusion in federated learning.