$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning

ePrint Report: $\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning

Harish Karthikeyan, Antigoni Polychroniadou

Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the single-server setting where a single evaluation server can securely aggregate client-held individual inputs. Our key contribution is One-shot Private Aggregation ($\mathsf{OPA}$) where …

aggregation applications challenges client clients communication computation cost eprint report federated federated learning high private report secure computation single work