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InfoResearchPeer-reviewed
Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment
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Summary
This paper proposes a federated learning method for radio frequency fingerprint identification (RFFI) that addresses performance loss from non-IID data across receivers. Each client learns aligned intermediate feature representations during local training to counter distribution shifts caused by receiver hardware, deployment location and channel conditions. On a real-world RF dataset, the method reaches 90.83% identification accuracy and shows higher stability than FedAvg and FedProx.