Performance Weighting for Robust Federated Learning Against Corrupted Sources. (arXiv:2205.01184v1 [cs.LG])

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 …

arxiv federated learning learning performance