  Federated learning is a technique that allows multiple entities to
collaboratively train models using their data without compromising data
privacy. However, despite its advantages, federated learning can be susceptible
to false data injection attacks. In these scenarios, a malicious entity with
control over specific agents in the network can manipulate the learning
process, leading to a suboptimal model. Consequently, addressing these data
injection attacks presents a significant research challenge in federated
learning systems. In this paper, we propose a novel technique to detect and
mitigate data injection attacks on federated learning systems. Our mitigation
method is a local scheme, performed during a single instance of training by the
coordinating node, allowing the mitigation during the convergence of the
algorithm. Whenever an agent is suspected to be an attacker, its data will be
ignored for a certain period, this decision will often be re-evaluated. We
prove that with probability 1, after a finite time, all attackers will be
ignored while the probability of ignoring a trustful agent becomes 0, provided
that there is a majority of truthful agents. Simulations show that when the
coordinating node detects and isolates all the attackers, the model recovers
and converges to the truthful model.
