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，往往会遗忘先前学过的信息。这一问题在联邦学习中尤为突出，因为联邦学习中的数据是分布式存储的，且每个用户的数据可能独立变化。已有许多解决方案旨在集中式环境下解决灾难性遗忘问题，但由于联邦学习的独特复杂性（如隐私顾虑和资源限制），这些方法无法直接适用。为应对这些挑战，本文提出了一种面向**联邦类增量学习**的框架，该框架利用生成模型从历史分布中合成样本，进而将合成数据与训练数据结合使用，以缓解灾难性遗忘。为保护隐私，生成模型在每个任务结束时在服务器端采用无数据方法进行训练，无需向客户端请求数据。此外，我们的解决方案不要求用户存储旧数据或模型，从而赋予用户随时加入或退出训练的自由。同时，我们引入了SuperImageNet——一种专为联邦持续学习定制的ImageNet数据集重组方案。通过多个数据集上的大量实验，我们证明了本方法相较于现有基线具有显著提升。