Mitigating Knowledge Forgetting by Generative Knowledge Replay and Forgetting-aware Aggregation in Semi-Supervised Federated Learning

Le résumé fourni par la source

Semi-supervised federated learning (SSFL) aims to leverage the vast amount of unlabeled data distributed across clients and a limited amount of labeled data held by the central server. However, SSFL faces a tough challenge of catastrophic forgetting, caused by discrepancies between local and global data distributions in non-independent and identically distributed (non-IID) settings, and exacerbated by noisy pseudo-labels. To deal with this problem, existing methods typically focus on mitigating the adverse effects of distribution divergence and refining the pseudo-labels. Differently, in this paper we tackle this problem from a data perspective by reducing the divergence between local and global distributions. Specifically, we propose global knowledge generative replay, which generates synthetic samples to complement the missing global knowledge during local training. Additionally, we introduce forgetting-aware model aggregation, a method that adaptively re-weights local models based on their degree of knowledge forgetting, resulting in a more robust global model. We conduct extensive experiments on widely-used benchmark datasets, and experimental results show that our method achieves state-of-the-art performance across various data settings, validating its effectiveness and superiority. The code will be available at https://github.com/lhq12/SemiFed.

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