Automatic and accurate vessel segmentation is crucial for disease diagnosis. Deep learning methods are widely used, but their promising results rely on accurately annotated data. Due to complex vessel morphology and low-contrast image, accurate vessel delineation poses a practical challenge, resulting in insufficient annotations, which is a prominent form of noisy labels. This paper proposes an Image-assisted Label Connective Completion method, which enhances label’s vessel information by images under the supervision of connectivity to address insufficient annotation issue. Specifically, we develop an Image-guided Vessel Enhancement module, which transmits structural information extracted from images based on label navigation to label space, promoting completion of missing annotated parts in original labels. In addition, a branch completion-connectivity loss is designed and introduced as an auxiliary supervision to prevent vessel branch disconnection during label completion. Experimental results on DRIVE, CHASE DB1 and DCA1 datasets demonstrate that our method outperforms existing noisy labels learning methods.

Federated Learning enables collaborative training across decentralized edge devices while preserving data privacy. However, fine-tuning large-scale pre-trained models in federated learning is hampered by substantial communication overhead and client resource limitations. Parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) reduce resource demands but suffer from aggregation discrepancies and heightened vulnerability to label noise, particularly in heterogeneous federated settings. In this paper, we introduce RFedLR, a robust federated PEFT framework designed to overcome these challenges. RFedLR integrates two key components: (1) Sensitivity-aware robust tuning, which identifies and selectively updates noise-sensitive parameters to bolster local robustness against label noise, and (2) Adaptive federated LoRA aggregation, which dynamically weights and aggregates LoRA updates based on their importance and stability to minimize bias and noise propagation. Comprehensive experimental validation shows RFedLR outperforms existing methods, achieving superior accuracy and robustness in noisy federated scenarios. Our code is available at: https://github.com/FangXiuwen/RFedLR

This paper studies a challenging robust federated learning task with model heterogeneous and data corrupted clients, where the clients have different local model structures. Data corruption is unavoidable due to factors, such as random noise, compression artifacts, or environmental conditions in real-world deployment, drastically crippling the entire federated system. To address these issues, this paper introduces a novel Robust Asymmetric Heterogeneous Federated Learning (RAHFL) framework. We propose a Diversity-enhanced supervised Contrastive Learning technique to enhance the resilience and adaptability of local models on various data corruption patterns. Its basic idea is to utilize complex augmented samples obtained by the mixed-data augmentation strategy for supervised contrastive learning, thereby enhancing the ability of the model to learn robust and diverse feature representations. Furthermore, we design an Asymmetric Heterogeneous Federated Learning strategy to resist corrupt feedback from external clients. The strategy allows clients to perform selective one-way learning during collaborative learning phase, enabling clients to refrain from incorporating lower-quality information from less robust or underperforming collaborators. Extensive experimental results demonstrate the effectiveness and robustness of our approach in diverse, challenging federated learning environments.

Federated Learning (FL) enables multiple devices to collaboratively train models without sharing their raw data. Considering that clients may prefer to design their own models independently, model heterogeneous FL has emerged. Additionally, due to the annotation uncertainty, the collected data usually contain unavoidable and varying noise, which cannot be effectively addressed by existing FL algorithms. This paper presents a novel solution that simultaneously handles model heterogeneity and label noise in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the model feedback by utilizing the easily-accessible public data, which does not require additional global models or relevant data for collaboration. (2) For internal label noise in each client, we design a dynamic label refinement strategy to mitigate the negative effects. (3) For challenging noisy feedback from other participants, we design an enhanced client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in mitigating the negative effects of various noise rates and types under both model homogeneous and heterogeneous FL settings.

Model heterogeneous federated learning is a realistic and challenging problem. However, due to the limitations of data collection, storage, and transmission conditions, as well as the existence of free-rider participants, the clients may suffer from data corruption. This paper starts the first attempt to investigate the problem of data corruption in the model heterogeneous federated learning framework. We design a novel method named Augmented Heterogeneous Federated Learning (AugHFL), which consists of two stages: 1) In the local update stage, a corruption-robust data augmentation strategy is adopted to minimize the adverse effects of local corruption while enabling the models to learn rich local knowledge. 2) In the collaborative update stage, we design a robust re-weighted communication approach, which implements communication between heterogeneous models while mitigating corrupted knowledge transfer from others. Extensive experiments demonstrate the effectiveness of our method in coping with various corruption patterns in the model heterogeneous federated learning setting.

Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding par-ticipant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication be-tween heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively as-signs corresponding weights to each client in the collabo-rative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative ef-fects of different noise rates/types under both model ho-mogeneous and heterogeneous federated learning settings, consistently outperforming existing methods.