# Evidential Federated Learning for Skin Lesion Image Classification

arXiv: https://arxiv.org/abs/2411.10071 · HTML: https://mlsanity.com/paper/2411.10071/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Federated, robust and private distillation, one of 12 directions in Knowledge distillation.
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Placed in

- Knowledge distillation / [Federated, robust and private distillation](https://mlsanity.com/distill/federated-robust-and-private-distillation/)

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Federated Knowledge Distillation](https://mlsanity.com/paper/2011.02367/) (2020)
- [Visual Prompt Based Personalized Federated Learning](https://mlsanity.com/paper/2303.08678/) (2023)
- [Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging](https://mlsanity.com/paper/2303.13033/) (2023)
- [A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning](https://mlsanity.com/paper/2206.05675/) (2022)
- [FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2206.10048/) (2022)
- [3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images](https://mlsanity.com/paper/2309.11015/) (2023)

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

- [RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility](https://mlsanity.com/paper/2503.16251/) (2025)
- [Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification](https://mlsanity.com/paper/2604.06032/) (2026)

## Abstract (arXiv)

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision Transformer (ViT) models trained in an evidential learning framework to maximize class evidences. Crucially, knowledge sharing across federation clients is achieved only through knowledge distillation on attention maps generated by the local ViT models, ensuring enhanced privacy preservation compared to traditional parameter or synthetic image sharing methodologies. FedEvPrompt is optimized within a round-based learning paradigm, where each round involves training local models followed by attention maps sharing with all federation clients. Experimental validation conducted in a real distributed setting, on the ISIC2019 dataset, demonstrates the superior performance of FedEvPrompt against baseline federated learning algorithms and knowledge distillation methods, without sharing model parameters. In conclusion, FedEvPrompt offers a promising approach for federated learning, effectively addressing challenges such as data heterogeneity, imbalance, privacy preservation, and knowledge sharing.