Survey of Privacy Threats and Countermeasures in Federated Learning
Masahiro Hayashitani Junki Mori Isamu Teranishi
Federated learning is widely considered to be as a privacy-aware learning
method because no training data is exchanged directly between clients.
Nevertheless, there are threats to privacy in federated learning, and privacy
countermeasures have been studied. However, we note that common and unique
privacy threats among typical types of federated learning have not been
categorized and described in a comprehensive and specific way. In this paper,
we describe privacy threats and countermeasures for the typical types of
federated learning; horizontal federated learning, vertical federated learning,
and transfer federated learning.