No Free Lunch Theorem for Security and Utility in Federated Learning
No Free Lunch Theorem for Security and Utility in Federated Learning
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of semi-honest partners; on …