# Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

arXiv: https://arxiv.org/abs/2003.09603 · HTML: https://mlsanity.com/paper/2003.09603/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Sparse Random Networks for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2209.15328/) (2022)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Training Mixed-Domain Translation Models via Federated Learning](https://mlsanity.com/paper/2205.01557/) (2022)
- [A Comprehensive Survey On Client Selections in Federated Learning](https://mlsanity.com/paper/2311.06801/) (2023)
- [Federated Natural Language Generation for Personalized Dialogue System](https://mlsanity.com/paper/2110.06419/) (2021)
- [FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization](https://mlsanity.com/paper/2406.18156/) (2024)
- [TinyMetaFed: Efficient Federated Meta-Learning for TinyML](https://mlsanity.com/paper/2307.06822/) (2023)
- [Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks](https://mlsanity.com/paper/2311.04350/) (2023)
- [Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth](https://mlsanity.com/paper/2405.03248/) (2024)
- [FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning](https://mlsanity.com/paper/2410.03042/) (2024)
- [BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection](https://mlsanity.com/paper/2212.11805/) (2022)
- [Bayesian Federated Model Compression for Communication and Computation Efficiency](https://mlsanity.com/paper/2404.07532/) (2024)
- [FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing](https://mlsanity.com/paper/2504.04867/) (2025)
- [A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges](https://mlsanity.com/paper/2208.12996/) (2022)

## Abstract (arXiv)

Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-k selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-k largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods' effectiveness.