# FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2109.04533 · HTML: https://mlsanity.com/paper/2109.04533/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [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)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (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)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average](https://mlsanity.com/paper/1806.05594/) (2018)
- [Adversarial Dropout for Supervised and Semi-supervised Learning](https://mlsanity.com/paper/1707.03631/) (2017)
- [Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective](https://mlsanity.com/paper/2002.11545/) (2020)
- [Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models](https://mlsanity.com/paper/2008.11364/) (2020)
- [LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data](https://mlsanity.com/paper/1811.12629/) (2018)
- [Exploiting Unlabeled Data in Smart Cities using Federated Learning](https://mlsanity.com/paper/2001.04030/) (2020)

## Built on by (most cited first, 11 of 11)

- [Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases](https://mlsanity.com/paper/2203.07345/) (2022)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [(FL)2: Overcoming Few Labels in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2410.23227/) (2024)
- [When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA](https://mlsanity.com/paper/2301.10114/) (2023)
- [MrTF: Model Refinery for Transductive Federated Learning](https://mlsanity.com/paper/2305.04201/) (2023)
- [FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients](https://mlsanity.com/paper/2402.10191/) (2024)
- [CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments](https://mlsanity.com/paper/2511.11778/) (2025)
- [Uncertainty Minimization for Personalized Federated Semi-Supervised Learning](https://mlsanity.com/paper/2205.02438/) (2022)
- [Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels](https://mlsanity.com/paper/2404.09861/) (2024)
- [Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains](https://mlsanity.com/paper/2505.21010/) (2025)
- [Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis](https://mlsanity.com/paper/2507.14181/) (2025)

## Abstract (arXiv)

Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine learning models with isolated yet unlabeled data. Most existing FedSSL methods focus on the classical scenario, i.e, the labeled and unlabeled data are stored at the client side. However, in real world applications, client users may not provide labels without any incentive. Thus, the scenario of labels at the server side is more practical. Since unlabeled data and labeled data are decoupled, most existing FedSSL approaches may fail to deal with such a scenario. To overcome this problem, in this paper, we propose FedCon, which introduces a new learning paradigm, i.e., contractive learning, to FedSSL. Experimental results on three datasets show that FedCon achieves the best performance with the contractive framework compared with state-of-the-art baselines under both IID and Non-IID settings. Besides, ablation studies demonstrate the characteristics of the proposed FedCon framework.