# Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity

arXiv: https://arxiv.org/abs/2405.16585 · HTML: https://mlsanity.com/paper/2405.16585/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 58 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 30 of 37)

- [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)
- [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)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2008.12161/) (2020)
- [FedProc: Prototypical Contrastive Federated Learning on Non-IID data](https://mlsanity.com/paper/2109.12273/) (2021)
- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier](https://mlsanity.com/paper/2303.10058/) (2023)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning](https://mlsanity.com/paper/2108.08435/) (2021)
- [Effects of Degradations on Deep Neural Network Architectures](https://mlsanity.com/paper/1807.10108/) (2018)
- [Cluster-driven Graph Federated Learning over Multiple Domains](https://mlsanity.com/paper/2104.14628/) (2021)
- [Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation](https://mlsanity.com/paper/2203.10144/) (2022)
- [Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning](https://mlsanity.com/paper/2309.16286/) (2023)
- [Improving Fairness via Federated Learning](https://mlsanity.com/paper/2110.15545/) (2021)

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

- [Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning](https://mlsanity.com/paper/2503.06457/) (2025)
- [Federated Learning with Domain Shift Eraser](https://mlsanity.com/paper/2503.13063/) (2025)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [Distributed Quasi-Newton Method for Fair and Fast Federated Learning](https://mlsanity.com/paper/2501.10877/) (2025)
- [Federated Out-of-Distribution Generalization: A Causal Augmentation View](https://mlsanity.com/paper/2504.19882/) (2025)
- [Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2505.04979/) (2025)
- [Tackling Data Heterogeneity in Federated Time Series Forecasting](https://mlsanity.com/paper/2411.15716/) (2024)
- [Global Intervention and Distillation for Federated Out-of-Distribution Generalization](https://mlsanity.com/paper/2504.00850/) (2025)
- [PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation](https://mlsanity.com/paper/2505.22522/) (2025)
- [Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation](https://mlsanity.com/paper/2511.10945/) (2025)
- [FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift](https://mlsanity.com/paper/2507.04781/) (2025)
- [Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout](https://mlsanity.com/paper/2507.10430/) (2025)
- [FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models](https://mlsanity.com/paper/2508.03356/) (2025)
- [Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling](https://mlsanity.com/paper/2505.24185/) (2025)
- [FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA](https://mlsanity.com/paper/2508.00873/) (2025)
- [DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models](https://mlsanity.com/paper/2602.19945/) (2026)
- [FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation](https://mlsanity.com/paper/2503.15390/) (2025)
- [Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization](https://mlsanity.com/paper/2504.21063/) (2025)
- [Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency](https://mlsanity.com/paper/2508.13518/) (2025)
- [Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis](https://mlsanity.com/paper/2509.18171/) (2025)
- [FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements](https://mlsanity.com/paper/2510.12132/) (2025)
- [Gains: Fine-grained Federated Domain Adaptation in Open Set](https://mlsanity.com/paper/2510.15967/) (2025)
- [Federated Learning at the Forefront of Fairness: A Multifaceted Perspective](https://mlsanity.com/paper/2602.00718/) (2026)
- [Domain-Skewed Federated Learning with Feature Decoupling and Calibration](https://mlsanity.com/paper/2603.14238/) (2026)
- [AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments](https://mlsanity.com/paper/2606.08197/) (2026)
- [EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models](https://mlsanity.com/paper/2608.08138/) (2026)
- [Out-of-Distribution Federated Distillation with Domain-Aware Proxy](https://mlsanity.com/paper/2608.08525/) (2026)
- [When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation](https://mlsanity.com/paper/2608.15639/) (2026)
- [Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation](https://mlsanity.com/paper/2610.04700/) (2026)
- [FedPCD: Priority-Constrained Federated Aggregation](https://mlsanity.com/paper/2610.13508/) (2026)

## Abstract (arXiv)

Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew, the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important parameters to potentially be overwhelmed by unimportant ones of dominant updates. It consequently results in significant performance decreases for lower-performing clients. 2) Model Aggregation Bias: existing FL approaches introduce unfair weight allocation and neglect domain diversity. It leads to biased model convergence objective and distinct performance among domains. We discover a pronounced directional update consistency in Federated Learning and propose a novel framework to tackle above issues. First, leveraging the discovered characteristic, we selectively discard unimportant parameter updates to prevent updates from clients with lower performance overwhelmed by unimportant parameters, resulting in fairer generalization performance. Second, we propose a fair aggregation objective to prevent global model bias towards some domains, ensuring that the global model continuously aligns with an unbiased model. The proposed method is generic and can be combined with other existing FL methods to enhance fairness. Comprehensive experiments on Digits and Office-Caltech demonstrate the high fairness and performance of our method.