Evidential Federated Learning for Skin Lesion Image Classification
This work addresses privacy-preserving and efficient knowledge sharing in federated learning for medical imaging, though it appears incremental as it combines existing techniques like evidential learning and prompt tuning.
The paper tackles skin lesion classification in a federated learning setting by introducing FedEvPrompt, which integrates evidential deep learning, prompt tuning, and knowledge distillation to share knowledge via attention maps without parameter sharing, achieving superior performance on the ISIC2019 dataset compared to baseline methods.
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.