# Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology

arXiv: https://arxiv.org/abs/2504.16732 · HTML: https://mlsanity.com/paper/2504.16732/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Model Merging by Uncertainty-Based Gradient Matching](https://mlsanity.com/paper/2310.12808/) (2023)
- [An Empirical Study of Multimodal Model Merging](https://mlsanity.com/paper/2304.14933/) (2023)
- [Fine-Tuning LLMs for Reliable Medical Question-Answering Services](https://mlsanity.com/paper/2410.16088/) (2024)
- [Optimized Biomedical Question-Answering Services with LLM and Multi-BERT Integration](https://mlsanity.com/paper/2410.12856/) (2024)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics](https://mlsanity.com/paper/2601.00277/) (2026)
- [Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies](https://mlsanity.com/paper/2601.00286/) (2026)

## Abstract (arXiv)

The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a textitSimplified Peer-to-Peer Swarm Learning (P2P-SL) Framework tailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust model synchronization while maintaining data privacy. Applied to cancer histopathology, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework's efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications.