 |
Finally, we evaluate overhead.
On the fast path, credit updates and feature extraction are constant-time operations per request with constant-size metadata lookup, so per-request processing overhead is lightweight.
On the control path, trust synchronization exchanges only top-M summaries every Tg seconds. With M equals 50 and Tg equals 2 seconds, per-node communication cost is bounded and remains small. In our simulations, the added gossip traffic stays below about 2% of observed control-plane traffic.
For federated learning, we communicate only model parameters rather than raw traffic. The model is lightweight, so coordination remains modest while still enabling cross-domain learning.
So overall, DT-NRS is designed to be practical for edge deployments where overhead must remain low. |
IEEE/ICACT20260343 Slide.27
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