Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study
Residential traffic classification supports service management, but learning across homes must account for heterogeneous traffic and privacy constraints. Privacy-aware training may impose uneven costs across traffic categories. We study this tradeoff in simulated two-client federated learning using 1.62 million preprocessed gateway-collected flows across six categories. We compare a full-feature baseline, feature suppression (FS), and differentially private stochastic gradient descent (DP-SGD) under one fixed record-level privacy setting. FS-mild excludes four timing features from 16 model inputs; it provides no formal privacy guarantee. With size-proportional aggregation, FS-mild achieve...
| Authors | Márton Pál Lipcsey-Magyar, Adrian Pekar |
|---|---|
| Primary category | cs.LG |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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