  Federated Learning has emerged as a dominant computational paradigm for
distributed machine learning. Its unique data privacy properties allow us to
collaboratively train models while offering participating clients certain
privacy-preserving guarantees. However, in real-world applications, a federated
environment may consist of a mixture of benevolent and malicious clients, with
the latter aiming to corrupt and degrade federated model's performance.
Different corruption schemes may be applied such as model poisoning and data
corruption. Here, we focus on the latter, the susceptibility of federated
learning to various data corruption attacks. We show that the standard global
aggregation scheme of local weights is inefficient in the presence of corrupted
clients. To mitigate this problem, we propose a class of task-oriented
performance-based methods computed over a distributed validation dataset with
the goal to detect and mitigate corrupted clients. Specifically, we construct a
robust weight aggregation scheme based on geometric mean and demonstrate its
effectiveness under random label shuffling and targeted label flipping attacks.
