Federated class-incremental learning (FCIL) aims to incrementally learn new classes across decentralized clients under non-IID data distributions. However, the pervasive challenge of label noise in FCIL has been completely overlooked. In this work, we introduce federated noisy class-incremental learning (FNCIL) and, for the first time, identify a novel form of label noise—spatio-temporal label misalignment—where samples from unseen classes are entirely mislabeled as known classes, with their correctly labeled counterparts appearing in latter tasks or other clients. This phenomenon undermines the effectiveness of existing centralized denoising strategies and creates a clear requirement for noise-robust methods in real-world FNCIL scenarios. To tackle this issue, we propose FedRNC, a dual-phase framework that leverages feature-space associations to establish spatio-temporal correspondences between clean global prototypes and noisy cached samples for progressive label correction. Experiments on standard benchmarks demonstrate FedRNC's superiority against existing baselines, along with its plug-and-play capability to upgrade FCIL systems for FNCIL.

Despite the promising potential of multi-modal learning in medical image segmentation, real-world applications often encounter modal incompleteness sourced from diverse domains and institutions, sparking significant discussions on incomplete multi-modal learning. Existing approaches either train a unified model for all or develop individual models for specific multi-modal combinations to ensure model fairness and robustness during inference. However, the assumption of complete multi-modal data for training is unrealistic and infeasible in clinical practice. In this paper, we thoroughly formulate such a challenging setting and propose hierarchical gradient alignment (HGA) to address uni- and multi-modal imbalance. Specifically, gradient direction is aligned through sequential meta learning for multi-modal combinations and multi-level self-distillation for uni-modals within each combination. Gradient magnitude is aligned based on relative preference estimation to balance the dominance of each modal during training. Extensive experiments on five public benchmarks (BraTS2018, BraTS2020, BraTS2023, MyoPS2020, and MSSEG2016) demonstrate that HGA consistently outperforms state-of-the-art incomplete and imbalanced multi-modal learning methods, as well as representative multi-task learning optimization techniques. More importantly, HGA is validated to work as plug-and-play modules for consistent performance improvement across different backbones. Code is available at https://github.com/Jun-Jie-Shi/HGA.

Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the performance ceilings of FL. In this paper, we, for the first time, identify and tackle this problem. For problem formulation, we propose a contour evolution for modeling non-independent and identically distributed (Non-IID) noise across pixels within each client and then extend it to the case of multi-source data to form a heterogeneous noise model (i.e., Non-IID annotation noise across clients). For robust learning from annotations with such two-level Non-IID noise, we emphasize the importance of data quality in model aggregation, allowing high-quality clients to have a greater impact on FL. To achieve this, we propose Federated learning with Annotation quAlity-aware AggregatIon, named FedA3I, by introducing a quality factor based on client-wise noise estimation. Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients. Extensive experiments on two real-world medical image segmentation datasets demonstrate the superior performance of FedA3I against the state-of-the-art approaches in dealing with cross-client annotation noise. The code is available at https://github.com/wnn2000/FedAAAI.