# Progressive Gated Residual Fusion

Personalized federated learning must balance shared knowledge against client-specific adaptation, but non-IID data and partial client participation let shared representations drift across communication rounds. FedEAGD keeps a client-specific exponential moving average teacher, measures drift as the cosine discrepancy between each sample's current and historical shared representations, and uses it to set historical distillation strength per sample while a stage-wise schedule introduces personalized residuals. On CIFAR-100 with Dirichlet concentration α = 0.1 it reaches 63.05% accuracy, 5.96 percentage points above the strongest evaluated baseline.

Personalized federated learning must balance shared knowledge against client-specific adaptation, but non-IID data and partial client participation let shared representations drift across communication rounds. FedEAGD keeps a client-specific exponential moving average teacher, measures drift as the cosine discrepancy between each sample's current and historical shared representations, and uses it to set historical distillation strength per sample while a stage-wise schedule introduces personalized residuals. On CIFAR-100 with Dirichlet concentration α = 0.1 it reaches 63.05% accuracy, 5.96 percentage points above the strongest evaluated baseline.