# FedMR: Fedreated Learning via Model Recombination

arXiv: https://arxiv.org/abs/2208.07677 · HTML: https://mlsanity.com/paper/2208.07677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 20 of 20)

- [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)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [The Skellam Mechanism for Differentially Private Federated Learning](https://mlsanity.com/paper/2110.04995/) (2021)
- [FLOP: Federated Learning on Medical Datasets using Partial Networks](https://mlsanity.com/paper/2102.05218/) (2021)
- [Efficient Device Scheduling with Multi-Job Federated Learning](https://mlsanity.com/paper/2112.05928/) (2021)
- [FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling](https://mlsanity.com/paper/2009.10748/) (2020)

## Abstract (arXiv)

As a promising privacy-preserving machine learning method, Federated Learning (FL) enables global model training across clients without compromising their confidential local data. However, existing FL methods suffer from the problem of low inference performance for unevenly distributed data, since most of them rely on Federated Averaging (FedAvg)-based aggregation. By averaging model parameters in a coarse manner, FedAvg eclipses the individual characteristics of local models, which strongly limits the inference capability of FL. Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models. To address the above issues, this paper proposes a novel and effective FL paradigm named FedMR (Federating Model Recombination). Unlike conventional FedAvg-based methods, the cloud server of FedMR shuffles each layer of collected local models and recombines them to achieve new models for local training on clients. Due to the fine-grained model recombination and local training in each FL round, FedMR can quickly figure out one globally optimal model for all the clients. Comprehensive experimental results demonstrate that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without causing extra communication overhead.