FedMix: Approximation of Mixup under Mean Augmented Federated Learning
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Abstract (arXiv)
Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current state-of-the-art algorithms suffer from performance degradation as the heterogeneity of local data across clients increases. To resolve this issue, we propose a simple framework, Mean Augmented Federated Learning (MAFL), where clients send and receive averaged local data, subject to the privacy requirements of target applications. Under our framework, we propose a new augmentation algorithm, named FedMix, which is inspired by a phenomenal yet simple data augmentation method, Mixup, but does not require local raw data to be directly shared among devices. Our method shows greatly improved performance in the standard benchmark datasets of FL, under highly non-iid federated settings, compared to conventional algorithms.
Key facts. Cited by 108 papers in the map (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 1 Jul 2021.
Builds on
29 of 41 references.
References · 29
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
17 more
- 2018Federated Learning with Non-IID Datacited by 1,108
- 2018LEAF: A Benchmark for Federated Settingscited by 792
- 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
- 2017Federated Multi-Task Learningcited by 672
- 2020Federated Learning with Matched Averagingcited by 564
- 2019Agnostic Federated Learningcited by 456
- 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
- 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
- 2019On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networkscited by 305
- 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
- 2017EMNIST: an extension of MNIST to handwritten letterscited by 213
- 2019Augmenting Data with Mixup for Sentence Classification: An Empirical Studycited by 125
- 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
- 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
- 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
- 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
- 2020Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixupcited by 29
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Citing papers per year: 2021: 8; 2022: 26; 2023: 29; 2024: 23; 2025: 16; 2026: 6.
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- 2026Separate Aggregation of Split Network for Personalized Federated Learning26 May 2026
- 2026Personalized Federated Learning via Gaussian Generative Modeling12 Mar 2026
- 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments26 Feb 2026
- 2026Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning24 Feb 2026
- 2025Geometric Prior-Guided Federated Prompt Calibration8 Dec 2025
- 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning24 Sep 2025
- 2025Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency19 Aug 2025
- 2025UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data9 Jun 2025
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