# Greedy Shapley Client Selection for Communication-Efficient Federated Learning

arXiv: https://arxiv.org/abs/2312.09108 · HTML: https://mlsanity.com/paper/2312.09108/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [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 Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [Game of Gradients: Mitigating Irrelevant Clients in Federated Learning](https://mlsanity.com/paper/2110.12257/) (2021)
- [Optimal Importance Sampling for Federated Learning](https://mlsanity.com/paper/2010.13600/) (2020)
- [Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning](https://mlsanity.com/paper/2106.10734/) (2021)
- [Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation](https://mlsanity.com/paper/2305.11633/) (2023)

## Built on by (most cited first, 7 of 7)

- [One-Shot Federated Learning with Classifier-Free Diffusion Models](https://mlsanity.com/paper/2502.08488/) (2025)
- [Maverick-Aware Shapley Valuation for Client Selection in Federated Learning](https://mlsanity.com/paper/2405.12590/) (2024)
- [Owen Sampling Accelerates Contribution Estimation in Federated Learning](https://mlsanity.com/paper/2508.21261/) (2025)
- [FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks](https://mlsanity.com/paper/2509.20193/) (2025)
- [Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation](https://mlsanity.com/paper/2602.03387/) (2026)
- [Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy](https://mlsanity.com/paper/2609.04945/) (2026)
- [Adaptive Bayesian Partner Selection for Federated Clinical Centers](https://mlsanity.com/paper/2609.16446/) (2026)

## Abstract (arXiv)

The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast convergence under practical settings characterized by significant heterogeneity in data distribution, computing, and communication resources across clients. For applications having timing constraints due to limited communication opportunities with the parameter server (PS), the client selection strategy is critical to complete model training within the fixed budget of communication rounds. To address this, we develop a biased client selection strategy, GreedyFed, that identifies and greedily selects the most contributing clients in each communication round. This method builds on a fast approximation algorithm for the Shapley Value at the PS, making the computation tractable for real-world applications with many clients. Compared to various client selection strategies on several real-world datasets, GreedyFed demonstrates fast and stable convergence with high accuracy under timing constraints and when imposing a higher degree of heterogeneity in data distribution, systems constraints, and privacy requirements.