# Federated Learning Aggregation: New Robust Algorithms with Guarantees

arXiv: https://arxiv.org/abs/2205.10864 · HTML: https://mlsanity.com/paper/2205.10864/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 9 of 9)

- [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)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [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)
- [Limited-Memory BFGS with Displacement Aggregation](https://mlsanity.com/paper/1903.03471/) (2019)

## Built on by (most cited first, 4 of 4)

- [Combating Exacerbated Heterogeneity for Robust Models in Federated Learning](https://mlsanity.com/paper/2303.00250/) (2023)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts](https://mlsanity.com/paper/2410.20250/) (2024)
- [Traversal Learning: A Lossless And Efficient Distributed Learning Framework](https://mlsanity.com/paper/2504.07471/) (2025)

## Abstract (arXiv)

Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to obtain a new more general "average" model (FedAvg). The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation. In this paper, we carry out a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. From this, we derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses. Moreover, we go beyond the assumptions introduced in theory, by evaluating the performance of these strategies and by comparing them with the one of FedAvg in classification tasks in both the IID and the Non-IID framework without additional hypothesis.