AI-aided dermatological diagnosis models require large amounts of high-quality data for effective training. While mobile devices make data collection easier, the lack of professional annotations and privacy concerns hinder their use in model training. Semi-supervised Federated Learning allows for collaborative training on both labeled and unlabeled data while protecting privacy. However, existing pseudo-labeling methods struggle with non-IID dermatosis data from different clients. To address this, we propose a multi-stage hierarchical semi-supervised federated learning framework (HSSFL), enabling labeled clients to assist unlabeled ones in generating pseudo-labels and maximizing data use. HSSFL consists of three stages: federated client clustering, iterative self-training, and opportunistic fine-tuning. Using correlation awareness for federated client clustering, followed by an ally-prioritized approach for self-training, enhances the reliability of pseudo-labels. Opportunistic fine-tuning further minimizes label errors. Experiments on both general and real-world dermatological datasets demonstrate that HSSFL outperforms state-of-the-art semi-supervised federated learning methods.

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios. In this article, we propose a novel FL method to discriminate the type of noise and make the FL mixed noise-robust, named FedMIN. FedMIN employs a composite framework that captures local-global differences in multiparticipant distributions to model generalized noise patterns. By determining adaptive thresholds for identifying mixed label noise in each client and assigning appropriate weights during model aggregation, FedMIN enhances the performance of the global model. Furthermore, FedMIN incorporates a loss alignment mechanism using local and global Gaussian mixture models (GMMs) to mitigate the risk of revealing samplewise loss. Extensive experiments are conducted on several public datasets, which include the simulated FL testbeds, i.e., CIFAR-10, CIFAR-100, and SVHN, and the real-world ones, i.e., Camelyon17 and multiorgan nuclei challenge (MoNuSAC). Compared to FL benchmarks, FedMIN improves model accuracy by up to 9.9% due to its superior noise estimation capabilities.

Meta-data, e.g., gender, medical history, etc., is of vital importance for dermatosis diagnosis clinically, while most existing AI-based methods ignore this information and only employ dermoscopy images. Due to the modality disequilibrium of image and meta-data, there would be unequal optimization during the modal fusion, resulting in the weak meta-data modality being suppressed and losing their contributions to the classification. To address this issue, we propose an Adaptive Multi-modal Fusion (AMF) method for dermatosis diagnosis, which reweights the fusion factor of image and meta-data by evaluating their optimization discrepancy through the Euclidean norm. What’s more, the Co-Attention mechanism is employed to align the two modalities implicitly. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on multi-modal dermatosis public datasets including ISIC 2019, PDA-UFES-20, and HAM10000. The results demonstrate that the proposed method can achieve better performance in comparison to the state-of-the-art methods.

Federated learning (FL) enables multiple clients to collaboratively train a globally generalized model while keeping local data decentralized. A key challenge in FL is to handle the heterogeneity of data distributions among clients. The local model will shift the global feature when fitting local data, which results in forgetting the global knowledge. Following the idea of knowledge distillation, the global model's prediction can be utilized to help local models preserve the global knowledge in FL. However, when the global model hasn't converged completely, its predictions tend to be less reliable on certain classes, which may results in distillation's misleading of local models. In this paper, we propose a class-wise adaptive self distillation (FedCAD) mechanism to ameliorate this problem. We design class-wise adaptive terms to soften the influence of distillation loss according to the global model's performance on each class and therefore avoid the misleading. Experiments show that our method outperforms other state-of-the-art FL algorithms on benchmark datasets.

Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants’ side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a Consensus-based Label Correction approach (CLC) in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is https://github.com/bixiao-zeng/CLC.git .