# FedControl: When Control Theory Meets Federated Learning

arXiv: https://arxiv.org/abs/2205.14236 · HTML: https://mlsanity.com/paper/2205.14236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 9 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 8 of 8)

- [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: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [SGD: General Analysis and Improved Rates](https://mlsanity.com/paper/1901.09401/) (2019)
- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)
- [FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.04755/) (2021)
- [FedCostWAvg: A new averaging for better Federated Learning](https://mlsanity.com/paper/2111.08649/) (2021)

## Abstract (arXiv)

To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. The technique is inspired from control theory and its classification performance is evaluated extensively in IID framework and compared with FedAvg.