Private and Distributionally Robust Federated Multiclass Classification
The work develops a differentially private framework for federated multiclass classification that combines distributionally robust optimization with sample-level privacy. It allows multiple clients to collaboratively train a linear classifier while accounting for uncertainty in data and labels, ensuring sensitive information is protected through noise injection during updates.