# Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization

arXiv: https://arxiv.org/abs/2208.11187 · HTML: https://mlsanity.com/paper/2208.11187/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2203.02110/) (2022)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning](https://mlsanity.com/paper/2007.03213/) (2020)
- [Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning](https://mlsanity.com/paper/2202.07470/) (2022)
- [Distributed Contrastive Learning for Medical Image Segmentation](https://mlsanity.com/paper/2208.03808/) (2022)
- [A Smartphone-Based Skin Disease Classification Using MobileNet CNN](https://mlsanity.com/paper/1911.07929/) (2019)
- [Evaluating the fairness of fine-tuning strategies in self-supervised learning](https://mlsanity.com/paper/2110.00538/) (2021)

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

- [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025)
- [An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals](https://mlsanity.com/paper/2407.15252/) (2024)
- [Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting](https://mlsanity.com/paper/2512.08733/) (2025)
- [Exploring the Impact of Skin Color on Skin Lesion Segmentation](https://mlsanity.com/paper/2603.29694/) (2026)

## Abstract (arXiv)

Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and intervention are often critical to prognosis and outcome. To this end, the past decade has witnessed the rapid evolvement of deep learning based smartphone apps, which allow users to conveniently and timely identify issues that have emerged around their skins. In order to collect sufficient data needed by deep learning and at the same time protect patient privacy, federated learning is often used, where individual clients aggregate a global model while keeping datasets local. However, existing federated learning frameworks are mostly designed to optimize the overall performance, while common dermatological datasets are heavily imbalanced. When applying federated learning to such datasets, significant disparities in diagnosis accuracy may occur. To address such a fairness issue, this paper proposes a fairness-aware federated learning framework for dermatological disease diagnosis. The framework is divided into two stages: In the first in-FL stage, clients with different skin types are trained in a federated learning process to construct a global model for all skin types. An automatic weight aggregator is used in this process to assign higher weights to the client with higher loss, and the intensity of the aggregator is determined by the level of difference between losses. In the latter post-FL stage, each client fine-tune its personalized model based on the global model in the in-FL stage. To achieve better fairness, models from different epochs are selected for each client to keep the accuracy difference of different skin types within 0.05. Experiments indicate that our proposed framework effectively improves both fairness and accuracy compared with the state-of-the-art.