  As deep learning applications become more prevalent, the need for extensive
training examples raises concerns for sensitive, personal, or proprietary data.
To overcome this, Federated Learning (FL) enables collaborative model training
across distributed data-owners, but it introduces challenges in safeguarding
model ownership and identifying the origin in case of a leak. Building upon
prior work, this paper explores the adaptation of black-and-white traitor
tracing watermarking to FL classifiers, addressing the threat of collusion
attacks from different data-owners. This study reveals that leak-resistant
white-box fingerprints can be directly implemented without a significant impact
from FL dynamics, while the black-box fingerprints are drastically affected,
losing their traitor tracing capabilities. To mitigate this effect, we propose
increasing the number of black-box salient neurons through dropout
regularization. Though there are still some open problems to be explored, such
as analyzing non-i.i.d. datasets and over-parameterized models, results show
that collusion-resistant traitor tracing, identifying all data-owners involved
in a suspected leak, is feasible in an FL framework, even in early stages of
training.
