# Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning Source: [Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:29:35.891Z Authors: Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Ag\\"uera y Arcas Published: 2022-08-19T16:26:03.000Z ## Abstract Federated learning (FL) is a framework for machine learning across heterogeneous client devices in a privacy-preserving fashion. To date, most FL algorithms learn a "global" server model across multiple rounds. At each round, the same server model is broadcast to all participating clients, updated locally, and then aggregated across clients. In this work, we propose a more general procedure in which clients "select" what values are sent to them. Notably, this allows clients to operate on smaller, data-dependent slices. In order to make this practical, we outline a primitive, federated select, which enables client-specific selection in realistic FL systems. We discuss how to use federated select for model training and show that it can lead to drastic reductions in communication and client memory usage, potentially enabling the training of models too large to fit on-device. We also discuss the implications of federated select on privacy and trust, which in turn affect possible system constraints and design. Finally, we discuss open questions concerning model architectures, privacy-preserving technologies, and practical FL systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Federated Image Classification on MNIST non-IID, α=0.1 (test)](): Federated Image Classification; MNIST non-IID, α=0.1 (test); Test Accuracy: 87.59. ## Original sources and implementation [Original paper]() [PDF]()