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

Source: [Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning](<https://www.sota2.com/research/paper/68fa6adb1e209131cea2ad89>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:35:16.571Z

Authors: Shaoxiong Ji, Wenqi Jiang, Anwar Walid, Xue Li

Published: 2020-03-21T08:20:04.000Z

## Abstract

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.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.09603>)

[PDF](<https://arxiv.org/pdf/2003.09603.pdf>)