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IEEE/ICACT20260343 Question.1 Questioner: tanghr@dsp.ac.cn 2026-02-07 오후 7:22:12	IEEE/ICACT20260343 Answer.1 Answer by Auhor hejf@dsp.ac.cn 2026-02-07 오후 7:22:12
How does the federated learning module integrate local anomaly detection with global model updates without sharing raw data?	In our framework, each resolution domain trains a lightweight anomaly detector locally on features extracted from its own resolution/caching behaviors, keeping raw traffic and logs on-site. During each federated round, the node sends only the updated model parameters (carrying a round identifier and sender signature) to the controller, which aggregates them with FedAvg to produce a new global model and distributes it back. The returned global model is then used for cross-domain anomaly detection and can also feed into the trust/credit adjustment and mitigation policies—without ever sharing raw data.

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