# Eliminating Domain Bias for Federated Learning in Representation Space

arXiv: https://arxiv.org/abs/2311.14975 · HTML: https://mlsanity.com/paper/2311.14975/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 95 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 30 of 59)

- [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)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [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)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [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)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport](https://mlsanity.com/paper/1805.09545/) (2018)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)

## Built on by (most cited first, 30 of 38)

- [Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models](https://mlsanity.com/paper/2406.10630/) (2024)
- [PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark](https://mlsanity.com/paper/2312.04992/) (2023)
- [An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning](https://mlsanity.com/paper/2403.15760/) (2024)
- [FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference](https://mlsanity.com/paper/2410.20105/) (2024)
- [DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices](https://mlsanity.com/paper/2412.05823/) (2024)
- [PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning](https://mlsanity.com/paper/2502.00354/) (2025)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning](https://mlsanity.com/paper/2410.07738/) (2024)
- [Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients](https://mlsanity.com/paper/2410.02845/) (2024)
- [Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation](https://mlsanity.com/paper/2407.16139/) (2024)
- [FedAH: Aggregated Head for Personalized Federated Learning](https://mlsanity.com/paper/2412.01295/) (2024)
- [Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning](https://mlsanity.com/paper/2503.13543/) (2025)
- [APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares](https://mlsanity.com/paper/2508.10732/) (2025)
- [Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity](https://mlsanity.com/paper/2404.03854/) (2024)
- [TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic](https://mlsanity.com/paper/2410.12316/) (2024)
- [Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes](https://mlsanity.com/paper/2501.08521/) (2025)
- [Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management](https://mlsanity.com/paper/2502.17694/) (2025)
- [FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2502.21012/) (2025)
- [Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation](https://mlsanity.com/paper/2506.01327/) (2025)
- [Improving Generalization in Heterogeneous Federated Continual Learning via Spatio-Temporal Gradient Matching with Prototypical Coreset](https://mlsanity.com/paper/2506.12031/) (2025)
- [FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning](https://mlsanity.com/paper/2511.22265/) (2025)
- [FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices](https://mlsanity.com/paper/2601.17713/) (2026)
- [SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport](https://mlsanity.com/paper/2604.06631/) (2026)
- [FedLog: Personalized Federated Classification with Less Communication and More Flexibility](https://mlsanity.com/paper/2407.08337/) (2024)
- [Adaptive Guidance for Local Training in Heterogeneous Federated Learning](https://mlsanity.com/paper/2410.06490/) (2024)
- [FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2502.07456/) (2025)
- [FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation](https://mlsanity.com/paper/2503.15390/) (2025)
- [FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning](https://mlsanity.com/paper/2504.12025/) (2025)
- [Whom to Trust? Adaptive Collaboration in Personalized Federated Learning](https://mlsanity.com/paper/2507.00259/) (2025)
- [Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective](https://mlsanity.com/paper/2507.12652/) (2025)

## Abstract (arXiv)

Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities. However, under statistically heterogeneous scenarios, we observe that biased data domains on clients cause a representation bias phenomenon and further degenerate generic representations during local training, i.e., the representation degeneration phenomenon. To address these issues, we propose a general framework Domain Bias Eliminator (DBE) for FL. Our theoretical analysis reveals that DBE can promote bi-directional knowledge transfer between server and client, as it reduces the domain discrepancy between server and client in representation space. Besides, extensive experiments on four datasets show that DBE can greatly improve existing FL methods in both generalization and personalization abilities. The DBE-equipped FL method can outperform ten state-of-the-art personalized FL methods by a large margin. Our code is public at https://github.com/TsingZ0/DBE.