Open-Set Recognition

TL;DR for operators A federated-learning client repeatedly downloads a model, computes locally, and uploads an update while the mobile network assigns radio resources to each step. Encryption hides the transmitted contents, but not the timing, direction, allocation size, and recurring cadence created by this training cycle. FLINT1 reconstructs those scheduling traces and uses them to classify CNN, RNN, and Transformer families. With a 300-second observation window, it reaches a macro F1 of 0.930 ± 0.021 in the evaluated closed-world testbed. ...

TL;DR for operators Before routing a user request, an automated service must first decide whether the request belongs to any capability it supports. A classifier forced to choose among known intents cannot answer that question reliably through low confidence alone. Xu, Kang, and Lü separate rejection from classification.1 Their gate represents each supported intent with several local acceptance regions because valid requests may form distinct semantic groups. It rejects a request when the request falls outside every region, after comparing each distance with that region’s learned acceptance radius. The method reports the highest out-of-scope F1 in all nine comparisons spanning three datasets and three known-intent ratios. ...