# Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data

arXiv: https://arxiv.org/abs/2310.18815 · HTML: https://mlsanity.com/paper/2310.18815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching](https://mlsanity.com/paper/2106.08600/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [RSCFed: Random Sampling Consensus Federated Semi-supervised Learning](https://mlsanity.com/paper/2203.13993/) (2022)
- [SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling](https://mlsanity.com/paper/2108.09412/) (2021)
- [SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision](https://mlsanity.com/paper/2110.02470/) (2021)

## Built on by (most cited first, 4 of 4)

- [Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection](https://mlsanity.com/paper/2402.05294/) (2024)
- [Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities](https://mlsanity.com/paper/2406.11636/) (2024)
- [F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics](https://mlsanity.com/paper/2411.11912/) (2024)
- [Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation](https://mlsanity.com/paper/2407.10327/) (2024)

## Abstract (arXiv)

The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data. This is particularly common in healthcare settings where collaborating partners (typically hospitals) may have images but not annotations. The bottleneck in this setting is the joint training of labeled and unlabeled clients as the objective function for each client varies based on the availability of labels. This paper investigates an alternative way for effective training with labeled and unlabeled clients in a federated setting. We propose a novel learning scheme specifically designed for SSFL which we call Isolated Federated Learning (IsoFed) that circumvents the problem by avoiding simple averaging of supervised and semi-supervised models together. In particular, our training approach consists of two parts - (a) isolated aggregation of labeled and unlabeled client models, and (b) local self-supervised pretraining of isolated global models in all clients. We evaluate our model performance on medical image datasets of four different modalities publicly available within the biomedical image classification benchmark MedMNIST. We further vary the proportion of labeled clients and the degree of heterogeneity to demonstrate the effectiveness of the proposed method under varied experimental settings.