Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study

2026-09-20

AI Summary

研究通过特征抑制和差分隐私方法解决家庭流量分类中的异质性和隐私问题，实验表明特征抑制在某些情况下优于差分隐私。

Abstract

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 achieves higher combined macro-F1 and worst-group F1 (the minimum per-class F1 across homes) than DP-SGD in all five seeds at both model capacities under stratified and temporal splits. The tested DP-SGD configuration incurs pronounced minority-category losses, especially in the smaller home, but FS-mild does not uniformly improve on the full-feature baseline. On stratified-split models, loss-based and shadow-model membership probes show near-chance aggregate discrimination without a consistent ranking across probes; this does not establish equivalent privacy. These findings support FS as an input-minimization baseline, not a substitute for formal privacy.

Problem

• Residential traffic classification
• Differential privacy
• Feature suppression
• Federated learning
• Privacy constraints

Innovation

• Feature Suppression
• Differential Privacy
• Residential Traffic Classification
• Federated Learning