Privacy-preserving federated learning (PPFL) is a paradigm of distributed privacy-preserving machine learning training in which a set of clients, each holding siloed training data, jointly compute a shared global model under the orchestration of an aggregation server. The system has the property that no party learns any information about any client’s training data, besides what could be inferred from the
