Robust Federated Training via Collaborative Machine Teaching using Trusted Instances

Understand

Federated learning performs distributed model training using local data hosted by agents.

• It shares only model parameter updates for iterative aggregation at the server.
• Although it is privacy-preserving by design, federated learning is vulnerable to noise corruption of local agents, as demonstrated in the previous study on adversarial data poisoning threat against federated learning systems.
• Even a single noise-corrupted agent can bias the model training.