# Client Selection in Federated Learning based on Gradients Importance

arXiv: https://arxiv.org/abs/2111.11204 · HTML: https://mlsanity.com/paper/2111.11204/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Sparse Communication for Distributed Gradient Descent](https://mlsanity.com/paper/1704.05021/) (2017)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [Communication-Efficient Federated Learning via Optimal Client Sampling](https://mlsanity.com/paper/2007.15197/) (2020)
- [On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning](https://mlsanity.com/paper/1911.08250/) (2019)
- [Stochastic Three Points Method for Unconstrained Smooth Minimization](https://mlsanity.com/paper/1902.03591/) (2019)

## Built on by (most cited first, 9 of 9)

- [Client Selection for Federated Policy Optimization with Environment Heterogeneity](https://mlsanity.com/paper/2305.10978/) (2023)
- [AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator](https://mlsanity.com/paper/2506.17805/) (2025)
- [Fast networked data selection via distributed smoothed quantile estimation](https://mlsanity.com/paper/2406.01929/) (2024)
- [XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing](https://mlsanity.com/paper/2503.12435/) (2025)
- [PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning](https://mlsanity.com/paper/2506.15923/) (2025)
- [Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach](https://mlsanity.com/paper/2508.09181/) (2025)
- [FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning](https://mlsanity.com/paper/2511.15454/) (2025)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)
- [Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning](https://mlsanity.com/paper/2608.07157/) (2026)

## Abstract (arXiv)

Federated learning (FL) enables multiple devices to collaboratively learn a global model without sharing their personal data. In real-world applications, the different parties are likely to have heterogeneous data distribution and limited communication bandwidth. In this paper, we are interested in improving the communication efficiency of FL systems. We investigate and design a device selection strategy based on the importance of the gradient norms. In particular, our approach consists of selecting devices with the highest norms of gradient values at each communication round. We study the convergence and the performance of such a selection technique and compare it to existing ones. We perform several experiments with non-iid set-up. The results show the convergence of our method with a considerable increase of test accuracy comparing to the random selection.