Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is distributed and can change independently for each user. Many solutions are proposed to resolve this catastrophic forgetting in a centralized setting. However, they do not apply directly to FL because of its unique complexities, such as privacy concerns and resource limitations. To overcome these challenges, this paper presents a framework for $\textbf{federated class incremental learning}$ that utilizes a generative model to synthesize samples from past distributions. This data can be later exploited alongside the training data to mitigate catastrophic forgetting. To preserve privacy, the generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Moreover, our solution does not demand the users to store old data or models, which gives them the freedom to join/leave the training at any time. Additionally, we introduce SuperImageNet, a new regrouping of the ImageNet dataset specifically tailored for federated continual learning. We demonstrate significant improvements compared to existing baselines through extensive experiments on multiple datasets.

翻译：深度学习模型在新数据上训练时，往往会遗忘先前学到的信息。这一问题在联邦学习（FL）中更为严重，因为联邦学习中数据分布在不同用户间独立变化。许多方法被提出用于解决集中式场景中的灾难性遗忘问题，但由于联邦学习特有的复杂性（如隐私顾虑和资源限制），这些方法无法直接适用。为克服这些挑战，本文提出了一种**联邦类增量学习**框架，该框架利用生成式模型合成来自先前分布的样本。这些数据随后可与训练数据结合使用，以缓解灾难性遗忘。为保护隐私，生成式模型在每个任务结束时采用无数据方法在服务器端训练，无需从客户端请求数据。此外，我们的解决方案不要求用户存储旧数据或模型，从而允许用户随时加入/退出训练。我们还引入了SuperImageNet——一种针对联邦持续学习专门设计的ImageNet数据集重组方案。通过在多个数据集上的广泛实验，我们证明了与现有基线方法相比的显著改进。