Abstract: Aspects of the invention include systems and methods configured for federated automatic machine learning. A non-limiting example computer-implemented method includes defining a search process including a model configuration for building an automatic machine learning pipeline definition and distributing the search process across a plurality of parties. Each member of the plurality of parties retains federated data including training data and holdout data. The method includes receiving, from each member of the plurality of parties, an evaluation result of the model configuration against respective holdout data and aggregating the received evaluation results to define aggregated parameters. A new pipeline definition is generated from the aggregated parameters and trained local models received from each member of the plurality of parties are aggregated to define an aggregated model. Each trained local model includes the new pipeline definition.