# Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

arXiv: https://arxiv.org/abs/2208.09432 · HTML: https://mlsanity.com/paper/2208.09432/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://mlsanity.com/paper/2008.03606/) (2020)
- [Language Modeling with Deep Transformers](https://mlsanity.com/paper/1905.04226/) (2019)
- [Papaya: Practical, Private, and Scalable Federated Learning](https://mlsanity.com/paper/2111.04877/) (2021)
- [On Large-Cohort Training for Federated Learning](https://mlsanity.com/paper/2106.07820/) (2021)
- [Federated Heavy Hitters Discovery with Differential Privacy](https://mlsanity.com/paper/1902.08534/) (2019)
- [Scaling Language Model Size in Cross-Device Federated Learning](https://mlsanity.com/paper/2204.09715/) (2022)
- [FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching](https://mlsanity.com/paper/2008.04975/) (2020)
- [Partial Variable Training for Efficient On-Device Federated Learning](https://mlsanity.com/paper/2110.05607/) (2021)
- [Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory](https://mlsanity.com/paper/2201.02664/) (2022)
- [Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation](https://mlsanity.com/paper/2111.02356/) (2021)
- [Efficient Image Representation Learning with Federated Sampled Softmax](https://mlsanity.com/paper/2203.04888/) (2022)

## Built on by (most cited first, 11 of 11)

- [FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction](https://mlsanity.com/paper/2212.01548/) (2022)
- [Can Public Large Language Models Help Private Cross-device Federated Learning?](https://mlsanity.com/paper/2305.12132/) (2023)
- [PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs](https://mlsanity.com/paper/2406.02958/) (2024)
- [Federated LoRA with Sparse Communication](https://mlsanity.com/paper/2406.05233/) (2024)
- [Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction](https://mlsanity.com/paper/2310.08670/) (2023)
- [DrJAX: Scalable and Differentiable MapReduce Primitives in JAX](https://mlsanity.com/paper/2403.07128/) (2024)
- [Towards a Better Theoretical Understanding of Independent Subnetwork Training](https://mlsanity.com/paper/2306.16484/) (2023)
- [Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat](https://mlsanity.com/paper/2309.03237/) (2023)
- [Federated Automatic Differentiation](https://mlsanity.com/paper/2301.07806/) (2023)
- [FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection](https://mlsanity.com/paper/2604.24012/) (2026)
- [Tackling Computational Heterogeneity in FL: A Few Theoretical Insights](https://mlsanity.com/paper/2307.06283/) (2023)

## Abstract (arXiv)

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.