# Towards Fair Federated Learning with Zero-Shot Data Augmentation

arXiv: https://arxiv.org/abs/2104.13417 · HTML: https://mlsanity.com/paper/2104.13417/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 27 Apr 2021.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [ZeroQ: A Novel Zero Shot Quantization Framework](https://mlsanity.com/paper/2001.00281/) (2020)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Overcoming Forgetting in Federated Learning on Non-IID Data](https://mlsanity.com/paper/1910.07796/) (2019)
- [Variational Federated Multi-Task Learning](https://mlsanity.com/paper/1906.06268/) (2019)
- [Universal Neural Machine Translation for Extremely Low Resource Languages](https://mlsanity.com/paper/1802.05368/) (2018)
- [MixKD: Towards Efficient Distillation of Large-scale Language Models](https://mlsanity.com/paper/2011.00593/) (2020)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [Federated Learning via Synthetic Data](https://mlsanity.com/paper/2008.04489/) (2020)
- [Analysis of Gender Inequality In Face Recognition Accuracy](https://mlsanity.com/paper/2002.00065/) (2020)
- [Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation](https://mlsanity.com/paper/2004.08945/) (2020)
- [Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No](https://mlsanity.com/paper/1911.06396/) (2019)
- [WAFFLe: Weight Anonymized Factorization for Federated Learning](https://mlsanity.com/paper/2008.05687/) (2020)

## Built on by (most cited first, 30 of 64)

- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [FedFed: Feature Distillation against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2310.05077/) (2023)
- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices](https://mlsanity.com/paper/2202.08036/) (2022)
- [Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2308.03457/) (2023)
- [Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction](https://mlsanity.com/paper/2209.15245/) (2022)
- [Architecture Agnostic Federated Learning for Neural Networks](https://mlsanity.com/paper/2202.07757/) (2022)
- [Acceleration of Federated Learning with Alleviated Forgetting in Local Training](https://mlsanity.com/paper/2203.02645/) (2022)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation](https://mlsanity.com/paper/2205.14900/) (2022)
- [Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks](https://mlsanity.com/paper/2209.15595/) (2022)
- [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022)
- [Federated Self-supervised Learning for Heterogeneous Clients](https://mlsanity.com/paper/2205.12493/) (2022)
- [Visual Prompt Based Personalized Federated Learning](https://mlsanity.com/paper/2303.08678/) (2023)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [Federated Learning from Small Datasets](https://mlsanity.com/paper/2110.03469/) (2021)
- [A Survey on Class Imbalance in Federated Learning](https://mlsanity.com/paper/2303.11673/) (2023)
- [Feature Matching Data Synthesis for Non-IID Federated Learning](https://mlsanity.com/paper/2308.04761/) (2023)
- [Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data](https://mlsanity.com/paper/2206.00686/) (2022)
- [Bias Propagation in Federated Learning](https://mlsanity.com/paper/2309.02160/) (2023)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning](https://mlsanity.com/paper/2503.06457/) (2025)
- [FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion](https://mlsanity.com/paper/2410.20380/) (2024)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Towards Multi-Objective Statistically Fair Federated Learning](https://mlsanity.com/paper/2201.09917/) (2022)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)

## Abstract (arXiv)

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.