KDIA uses a triFreqs-weighted all-client teacher model plus knowledge distillation and a conditional generator to improve accuracy and convergence in large-client, low-participation heterogeneous federated learning.
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Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation
KDIA uses a triFreqs-weighted all-client teacher model plus knowledge distillation and a conditional generator to improve accuracy and convergence in large-client, low-participation heterogeneous federated learning.