CEFGL combines low-rank global and sparse personalized model components with quantization and communication skipping to improve accuracy and cut communication cost in federated graph classification.
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Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
CEFGL combines low-rank global and sparse personalized model components with quantization and communication skipping to improve accuracy and cut communication cost in federated graph classification.