# Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning

arXiv: https://arxiv.org/abs/2107.10663 · HTML: https://mlsanity.com/paper/2107.10663/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 65 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [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)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://mlsanity.com/paper/1902.06720/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [On Exact Computation with an Infinitely Wide Neural Net](https://mlsanity.com/paper/1904.11955/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)

## Built on by (most cited first, 23 of 23)

- [Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization](https://mlsanity.com/paper/2203.09747/) (2022)
- [Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring](https://mlsanity.com/paper/2206.06818/) (2022)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [Federated Data Analytics: A Study on Linear Models](https://mlsanity.com/paper/2206.07786/) (2022)
- [Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation](https://mlsanity.com/paper/2406.12815/) (2024)
- [Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling](https://mlsanity.com/paper/2111.14008/) (2021)
- [Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective](https://mlsanity.com/paper/2411.07523/) (2024)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations](https://mlsanity.com/paper/2304.14976/) (2023)
- [Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs](https://mlsanity.com/paper/2206.04687/) (2022)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Federated Learning over Connected Modes](https://mlsanity.com/paper/2403.03333/) (2024)
- [FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity](https://mlsanity.com/paper/2211.07248/) (2022)
- [Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation](https://mlsanity.com/paper/2309.17097/) (2023)
- [Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review](https://mlsanity.com/paper/2409.16340/) (2024)
- [Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg](https://mlsanity.com/paper/2508.12576/) (2025)
- [FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning](https://mlsanity.com/paper/2312.17029/) (2023)
- [GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques](https://mlsanity.com/paper/2212.04103/) (2022)
- [Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization](https://mlsanity.com/paper/2308.16870/) (2023)
- [Query-based Knowledge Transfer for Heterogeneous Learning Environments](https://mlsanity.com/paper/2504.09205/) (2025)
- [Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling](https://mlsanity.com/paper/2504.13462/) (2025)
- [SHEFL: Sparse Heterogeneous Ensemble Federated Learning with Group-Balanced Aggregation](https://mlsanity.com/paper/2508.08552/) (2025)
- [Federated Ensemble Learning with Progressive Model Personalization](https://mlsanity.com/paper/2602.14244/) (2026)

## Abstract (arXiv)

In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL). Instead of aggregating localmodels to update a single global model, Fed-ensemble uses random permutations to update a group of K models and then obtains predictions through model averaging. Fed-ensemble can be readily utilized within established FL methods and does not impose a computational overhead as it only requires one of the K models to be sent to a client in each communication round. Theoretically, we show that predictions on newdata from all K models belong to the same predictive posterior distribution under a neural tangent kernel regime. This result in turn sheds light onthe generalization advantages of model averaging. We also illustrate thatFed-ensemble has an elegant Bayesian interpretation. Empirical results show that our model has superior performance over several FL algorithms,on a wide range of data sets, and excels in heterogeneous settings often encountered in FL applications.