# Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

arXiv: https://arxiv.org/abs/2002.11545 · HTML: https://mlsanity.com/paper/2002.11545/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

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

- [Divergence-aware Federated Self-Supervised Learning](https://mlsanity.com/paper/2204.04385/) (2022)
- [Collaborative Unsupervised Visual Representation Learning from Decentralized Data](https://mlsanity.com/paper/2108.06492/) (2021)
- [SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training](https://mlsanity.com/paper/2106.01432/) (2021)
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- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [FedCon: A Contrastive Framework for Federated Semi-Supervised Learning](https://mlsanity.com/paper/2109.04533/) (2021)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data](https://mlsanity.com/paper/2203.01752/) (2022)
- [Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators](https://mlsanity.com/paper/2307.05358/) (2023)
- [Federated Semi-Supervised Learning with Prototypical Networks](https://mlsanity.com/paper/2205.13921/) (2022)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)
- [Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones](https://mlsanity.com/paper/2201.01230/) (2022)
- [Towards Unbiased Training in Federated Open-world Semi-supervised Learning](https://mlsanity.com/paper/2305.00771/) (2023)
- [Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients](https://mlsanity.com/paper/2405.16304/) (2024)
- [Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning](https://mlsanity.com/paper/2202.02611/) (2022)
- [Federated Self-Training for Semi-Supervised Audio Recognition](https://mlsanity.com/paper/2107.06877/) (2021)
- [FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning](https://mlsanity.com/paper/2110.07829/) (2021)
- [Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection](https://mlsanity.com/paper/2310.17097/) (2023)
- [Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning](https://mlsanity.com/paper/2109.14611/) (2021)
- [Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design](https://mlsanity.com/paper/2110.13388/) (2021)
- [TinyMLOps: Operational Challenges for Widespread Edge AI Adoption](https://mlsanity.com/paper/2203.10923/) (2022)
- [FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis](https://mlsanity.com/paper/2302.11823/) (2023)
- [FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients](https://mlsanity.com/paper/2402.10191/) (2024)
- [Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning](https://mlsanity.com/paper/2211.07364/) (2022)
- [Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt](https://mlsanity.com/paper/2310.04455/) (2023)
- [Learn How to Query from Unlabeled Data Streams in Federated Learning](https://mlsanity.com/paper/2412.08138/) (2024)
- [SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning](https://mlsanity.com/paper/2412.13589/) (2024)

## Abstract (arXiv)

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner. Yet, in most applications of FL, such as keyboard prediction, labeling data requires virtually no additional efforts, which is not generally the case. In reality, acquiring large-scale labeled datasets can be extremely costly, which motivates research works that exploit unlabeled data to help build machine learning models. However, to the best of our knowledge, few existing works aim to utilize unlabeled data to enhance federated learning, which leaves a potentially promising research topic. In this paper, we identify the need to exploit unlabeled data in FL, and survey possible research fields that can contribute to the goal.