# SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2511.14302 · HTML: https://mlsanity.com/paper/2511.14302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [Parameterized Knowledge Transfer for Personalized Federated Learning](https://mlsanity.com/paper/2111.02862/) (2021)
- [Semi-supervised Semantic Segmentation with Error Localization Network](https://mlsanity.com/paper/2204.02078/) (2022)
- [SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training](https://mlsanity.com/paper/2106.01432/) (2021)
- [RSCFed: Random Sampling Consensus Federated Semi-supervised Learning](https://mlsanity.com/paper/2203.13993/) (2022)
- [Knowledge Distillation for Federated Learning: a Practical Guide](https://mlsanity.com/paper/2211.04742/) (2022)
- [Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training](https://mlsanity.com/paper/2307.16660/) (2023)
- [Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition](https://mlsanity.com/paper/2501.17628/) (2025)

## Abstract (arXiv)

Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (FSSL) offers a solution but faces two challenges: pseudo-label reliability depends on the strength of local models, and client devices often require compact or heterogeneous architectures due to limited computational resources. These constraints reduce the quality and stability of pseudo-labels, while large models, though more accurate, cannot be trained or used for routine inference on client devices. We propose SAM-Fed, a federated semi-supervised framework that leverages a high-capacity segmentation foundation model to guide lightweight clients during training. SAM-Fed combines dual knowledge distillation with an adaptive agreement mechanism to refine pixel-level supervision. Experiments on skin lesion and polyp segmentation across homogeneous and heterogeneous settings show that SAM-Fed consistently outperforms state-of-the-art FSSL methods.