  Existing model poisoning attacks to federated learning assume that an
attacker has access to a large fraction of compromised genuine clients.
However, such assumption is not realistic in production federated learning
systems that involve millions of clients. In this work, we propose the first
Model Poisoning Attack based on Fake clients called MPAF. Specifically, we
assume the attacker injects fake clients to a federated learning system and
sends carefully crafted fake local model updates to the cloud server during
training, such that the learnt global model has low accuracy for many
indiscriminate test inputs. Towards this goal, our attack drags the global
model towards an attacker-chosen base model that has low accuracy.
Specifically, in each round of federated learning, the fake clients craft fake
local model updates that point to the base model and scale them up to amplify
their impact before sending them to the cloud server. Our experiments show that
MPAF can significantly decrease the test accuracy of the global model, even if
classical defenses and norm clipping are adopted, highlighting the need for
more advanced defenses.
