  The increasing complexity of IT systems requires solutions, that support
operations in case of failure. Therefore, Artificial Intelligence for System
Operations (AIOps) is a field of research that is becoming increasingly
focused, both in academia and industry. One of the major issues of this area is
the lack of access to adequately labeled data, which is majorly due to legal
protection regulations or industrial confidentiality. Methods to mitigate this
stir from the area of federated learning, whereby no direct access to training
data is required. Original approaches utilize a central instance to perform the
model synchronization by periodical aggregation of all model parameters.
However, there are many scenarios where trained models cannot be published
since its either confidential knowledge or training data could be reconstructed
from them. Furthermore the central instance needs to be trusted and is a single
point of failure. As a solution, we propose a fully decentralized approach,
which allows to share knowledge between trained models. Neither original
training data nor model parameters need to be transmitted. The concept relies
on teacher and student roles that are assigned to the models, whereby students
are trained on the output of their teachers via synthetically generated input
data. We conduct a case study on log anomaly detection. The results show that
an untrained student model, trained on the teachers output reaches comparable
F1-scores as the teacher. In addition, we demonstrate that our method allows
the synchronization of several models trained on different distinct training
data subsets.
