# Optimising Communication Overhead in Federated Learning Using NSGA-II

arXiv: https://arxiv.org/abs/2204.02183 · HTML: https://mlsanity.com/paper/2204.02183/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Gradient Sparsification for Communication-Efficient Distributed Optimization](https://mlsanity.com/paper/1710.09854/) (2017)
- [QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding](https://mlsanity.com/paper/1610.02132/) (2016)
- [Ternary Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2003.03564/) (2020)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [Communication-Efficient Federated Learning with Compensated Overlap-FedAvg](https://mlsanity.com/paper/2012.06706/) (2020)
- [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020)

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

- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)
- [Probably Approximately Correct Federated Learning](https://mlsanity.com/paper/2304.04641/) (2023)
- [FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training](https://mlsanity.com/paper/2504.03783/) (2025)
- [Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning](https://mlsanity.com/paper/2504.03077/) (2025)
- [GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques](https://mlsanity.com/paper/2212.04103/) (2022)
- [Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning](https://mlsanity.com/paper/2505.15836/) (2025)

## Abstract (arXiv)

Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead stands in achieving this without decreasing the model's efficiency (contradictory relation). To do so, many works investigated the compression of the pre/mid/post-trained models and the communication rounds, separately, although they jointly contribute to the communication overload. Our work aims at optimising communication overhead in federated learning by (I) modelling it as a multi-objective problem and (II) applying a multi-objective optimization algorithm (NSGA-II) to solve it. To the best of the author's knowledge, this is the first work that texttt(I) explores the add-in that evolutionary computation could bring for solving such a problem, and texttt(II) considers both the neuron and devices features together. We perform the experimentation by simulating a server/client architecture with 4 slaves. We investigate both convolutional and fully-connected neural networks with 12 and 3 layers, 887,530 and 33,400 weights, respectively. We conducted the validation on the textttMNIST dataset containing 70,000 images. The experiments have shown that our proposal could reduce communication by 99% and maintain an accuracy equal to the one obtained by the FedAvg Algorithm that uses 100% of communications.