mlsanityData to 14 Oct 2026

Histopathological Image Classification and Vulnerability Analysis using Federated Learning

arXiv 2310.07380 ↗First posted 11 Oct 2023cs.LG cs.AIcited by 1

On the map: LLM safety and security → Federated unlearning and poisoning

Abstract (arXiv)

Healthcare is one of the foremost applications of machine learning (ML). Traditionally, ML models are trained by central servers, which aggregate data from various distributed devices to forecast the results for newly generated data. This is a major concern as models can access sensitive user information, which raises privacy concerns. A federated learning (FL) approach can help address this issue: A global model sends its copy to all clients who train these copies, and the clients send the updates (weights) back to it. Over time, the global model improves and becomes more accurate. Data privacy is protected during training, as it is conducted locally on the clients' devices. However, the global model is susceptible to data poisoning. We develop a privacy-preserving FL technique for a skin cancer dataset and show that the model is prone to data poisoning attacks. Ten clients train the model, but one of them intentionally introduces flipped labels as an attack. This reduces the accuracy of the global model. As the percentage of label flipping increases, there is a noticeable decrease in accuracy. We use a stochastic gradient descent optimization algorithm to find the most optimal accuracy for the model. Although FL can protect user privacy for healthcare diagnostics, it is also vulnerable to data poisoning, which must be addressed.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 8 of its 21 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

8 of 21 references.

References · 8

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  3. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  4. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  5. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 80
  6. 2019Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Datacited by 43
  7. 2021Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classificationcited by 23
  8. 2017Skin Lesion Classification Using Hybrid Deep Neural Networkscited by 14

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detectioncited by 13

Similar, not cited

Close in wording, no citation either way

  1. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillation
  2. 2024Image Distillation for Safe Data Sharing in Histopathology
  3. 2025FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification
  4. 2024Privacy in Federated Learning
  5. 2026Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
  6. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
  7. 2026FedBiCross: Personalized One-Shot Federated Learning on Medical Images
  8. 2025Securing Genomic Data Against Inference Attacks in Federated Learning Environments