Buffering client data in federated QUIC classification suppresses training-time volatility, yet the headline 95.2 percent F1 is measured on a buffered test set that hides real-time traffic swings.
Wcl: Client selection in federated learning with a combination of model weight divergence and client training loss for internet traffic classification,
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Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Buffering client data in federated QUIC classification suppresses training-time volatility, yet the headline 95.2 percent F1 is measured on a buffered test set that hides real-time traffic swings.