# Submodular Maximization Approaches for Equitable Client Selection in Federated Learning

arXiv: https://arxiv.org/abs/2408.13683 · HTML: https://mlsanity.com/paper/2408.13683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Lazier Than Lazy Greedy](https://mlsanity.com/paper/1409.7938/) (2014)
- [Faster Non-Convex Federated Learning via Global and Local Momentum](https://mlsanity.com/paper/2012.04061/) (2020)
- [Robust Training in High Dimensions via Block Coordinate Geometric Median Descent](https://mlsanity.com/paper/2106.08882/) (2021)
- [Discrete Signal Processing with Set Functions](https://mlsanity.com/paper/2001.10290/) (2020)
- [Randomized Greedy Sensor Selection: Leveraging Weak Submodularity](https://mlsanity.com/paper/1807.08627/) (2018)
- [A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks](https://mlsanity.com/paper/1709.08823/) (2017)
- [Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise](https://mlsanity.com/paper/2307.07406/) (2023)
- [Near-Optimal Sparse Sensing for Gaussian Detection with Correlated Observations](https://mlsanity.com/paper/1710.09676/) (2017)
- [Distributed Maximization of Submodular and Approximately Submodular Functions](https://mlsanity.com/paper/2009.12992/) (2020)
- [Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations](https://mlsanity.com/paper/2404.03740/) (2024)
- [Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints](https://mlsanity.com/paper/1807.07650/) (2018)
- [Localized Distributional Robustness in Submodular Multi-Task Subset Selection](https://mlsanity.com/paper/2404.03759/) (2024)

## Built on by (most cited first, 1 of 1)

- [Equitable Federated Learning with Activation Clustering](https://mlsanity.com/paper/2410.19207/) (2024)

## Abstract (arXiv)

In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random selection often leads to disparate performance among clients, raising concerns regarding fairness, particularly in applications where equitable outcomes are crucial, such as in medical or financial machine learning tasks. This disparity typically becomes more pronounced with the advent of performance-centric client sampling techniques. This paper introduces two novel methods, namely SUBTRUNC and UNIONFL, designed to address the limitations of random client selection. Both approaches utilize submodular function maximization to achieve more balanced models. By modifying the facility location problem, they aim to mitigate the fairness concerns associated with random selection. SUBTRUNC leverages client loss information to diversify solutions, while UNIONFL relies on historical client selection data to ensure a more equitable performance of the final model. Moreover, these algorithms are accompanied by robust theoretical guarantees regarding convergence under reasonable assumptions. The efficacy of these methods is demonstrated through extensive evaluations across heterogeneous scenarios, revealing significant improvements in fairness as measured by a client dissimilarity metric.