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. Our code and models are public available at https://github.com/FangXiuwen/RAHFL.

本研究解决了存在模型异构和数据受损客户端的稳健联邦学习任务。提出了一种新颖的稳健非对称异构联邦学习框架（RAHFL）和增强多样性的监督对比学习技术，以提高模型在面对不同数据损坏模式时的韧性和适应性。实验结果表明，该方法在多种复杂的联邦学习环境中具有显著的有效性和稳健性。

具有受损客户端的稳健非对称异构联邦学习