# FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning

arXiv: https://arxiv.org/abs/2210.07615 · HTML: https://mlsanity.com/paper/2210.07615/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 73 references that are in the map.
- First posted on arXiv 14 Oct 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [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)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [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)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [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)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)

## Built on by (most cited first, 27 of 27)

- [Personalized Federated Learning with Feature Alignment and Classifier Collaboration](https://mlsanity.com/paper/2306.11867/) (2023)
- [FedDisco: Federated Learning with Discrepancy-Aware Collaboration](https://mlsanity.com/paper/2305.19229/) (2023)
- [No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier](https://mlsanity.com/paper/2303.10058/) (2023)
- [Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors](https://mlsanity.com/paper/2405.11525/) (2024)
- [Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10066/) (2024)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2410.18478/) (2024)
- [Federated Learning on Virtual Heterogeneous Data with Local-global Distillation](https://mlsanity.com/paper/2303.02278/) (2023)
- [Fake It Till Make It: Federated Learning with Consensus-Oriented Generation](https://mlsanity.com/paper/2312.05966/) (2023)
- [Personalised Federated Learning On Heterogeneous Feature Spaces](https://mlsanity.com/paper/2301.11447/) (2023)
- [You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data](https://mlsanity.com/paper/2503.06916/) (2025)
- [Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective](https://mlsanity.com/paper/2502.13732/) (2025)
- [Federated Learning Empowered by Generative Content](https://mlsanity.com/paper/2312.05807/) (2023)
- [FedSheafHN: Personalized Federated Learning on Graph-structured Data](https://mlsanity.com/paper/2405.16056/) (2024)
- [FedEL: Federated Elastic Learning for Heterogeneous Devices](https://mlsanity.com/paper/2509.16902/) (2025)
- [Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.01816/) (2023)
- [FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units](https://mlsanity.com/paper/2401.12862/) (2024)
- [Federated Contrastive Learning for Personalized Semantic Communication](https://mlsanity.com/paper/2406.09182/) (2024)
- [FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services](https://mlsanity.com/paper/2411.10595/) (2024)
- [FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning](https://mlsanity.com/paper/2502.16832/) (2025)
- [Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning](https://mlsanity.com/paper/2403.06535/) (2024)
- [Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity](https://mlsanity.com/paper/2405.20046/) (2024)
- [Reducing Spurious Correlation for Federated Domain Generalization](https://mlsanity.com/paper/2407.19174/) (2024)
- [FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection](https://mlsanity.com/paper/2504.11216/) (2025)
- [FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs](https://mlsanity.com/paper/2504.21206/) (2025)
- [Personalized Federated Learning via Gaussian Generative Modeling](https://mlsanity.com/paper/2603.11620/) (2026)
- [CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning](https://mlsanity.com/paper/2608.02222/) (2026)

## Abstract (arXiv)

One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address this issue, we propose a novel method FedFM, which guides each client's features to match shared category-wise anchors (landmarks in feature space). This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space. Besides, we tackle the challenge of varying objective function and provide convergence guarantee for FedFM. In FedFM, to mitigate the phenomenon of overlapping feature spaces across categories and enhance the effectiveness of feature matching, we further propose a more precise and effective feature matching loss called contrastive-guiding (CG), which guides each local feature to match with the corresponding anchor while keeping away from non-corresponding anchors. Additionally, to achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs. Through extensive experiments, we demonstrate that FedFM with CG outperforms several works by quantitative and qualitative comparisons. FedFM-Lite can achieve better performance than state-of-the-art methods with five to ten times less communication costs.