# FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing

arXiv: https://arxiv.org/abs/2504.04867 · HTML: https://mlsanity.com/paper/2504.04867/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 7 Apr 2025.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)
- [Federated Learning With Quantized Global Model Updates](https://mlsanity.com/paper/2006.10672/) (2020)
- [Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection](https://mlsanity.com/paper/2002.09692/) (2020)
- [Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2003.09603/) (2020)
- [Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation](https://mlsanity.com/paper/2101.10904/) (2021)

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

- [An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning](https://mlsanity.com/paper/2504.08356/) (2025)

## Abstract (arXiv)

Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users' behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run.