  The Radio frequency (RF) fingerprinting technique makes highly secure device
authentication possible for future networks by exploiting hardware
imperfections introduced during manufacturing. Although this technique has
received considerable attention over the past few years, RF fingerprinting
still faces great challenges of channel-variation-induced data distribution
drifts between the training phase and the test phase. To address this
fundamental challenge and support model training and testing at the edge, we
propose a federated RF fingerprinting algorithm with a novel strategy called
model transfer and adaptation (MTA). The proposed algorithm introduces dense
connectivity among convolutional layers into RF fingerprinting to enhance
learning accuracy and reduce model complexity. Besides, we implement the
proposed algorithm in the context of federated learning, making our algorithm
communication efficient and privacy-preserved. To further conquer the data
mismatch challenge, we transfer the learned model from one channel condition
and adapt it to other channel conditions with only a limited amount of
information, leading to highly accurate predictions under environmental drifts.
Experimental results on real-world datasets demonstrate that the proposed
algorithm is model-agnostic and also signal-irrelevant. Compared with
state-of-the-art RF fingerprinting algorithms, our algorithm can improve
prediction performance considerably with a performance gain of up to 15\%.
