  Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses
data privacy, security, access rights and access to heterogeneous information
issues by training a global model using distributed nodes. Despite its
advantages, there is an increased potential for cyberattacks on FL-based ML
techniques that can undermine the benefits. Model-poisoning attacks on FL
target the availability of the model. The adversarial objective is to disrupt
the training. We propose attestedFL, a defense mechanism that monitors the
training of individual nodes through state persistence in order to detect a
malicious worker. A fine-grained assessment of the history of the worker
permits the evaluation of its behavior in time and results in innovative
detection strategies. We present three lines of defense that aim at assessing
if the worker is reliable by observing if the node is really training,
advancing towards a goal. Our defense exposes an attacker's malicious behavior
and removes unreliable nodes from the aggregation process so that the FL
process converge faster. Through extensive evaluations and against various
adversarial settings, attestedFL increased the accuracy of the model between
12% to 58% under different scenarios such as attacks performed at different
stages of convergence, attackers colluding and continuous attacks.
