一种缓解视觉任务中联邦类增量学习灾难性遗忘的无数据方法
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
AI总结：
本文提出一个联邦类增量学习框架，利用在服务器端通过无数据方法训练的生成模型合成旧分布样本，无需客户端存储旧数据或模型，有效缓解灾难性遗忘，并引入SuperImageNet基准，实验证明取得显著改进。
摘要：
深度学习模型在新数据上训练时经常会遗忘之前学到的信息。这个问题在联邦学习（FL）中更加严重，因为数据是分布式的，并且每个用户的数据可以独立变化。许多解决方案被提出以解决集中式环境中的灾难性遗忘问题。然而，由于联邦学习具有独特的复杂性，如隐私问题和资源限制，这些方案不能直接应用于FL。为了克服这些挑战，本文提出了一个联邦类增量学习框架，利用生成模型从过去的分布中合成样本。这些数据随后可以与训练数据一起利用，以缓解灾难性遗忘。为了保护隐私，生成模型在服务器端使用无数据方法在每个任务结束时进行训练，而不向客户端请求数据。此外，我们的方案不要求用户存储旧数据或模型，这使得他们可以随时加入或退出训练。另外，我们引入了SuperImageNet，这是对ImageNet数据集的重新分组，专门为联邦持续学习量身定制。通过在多个数据集上的广泛实验，我们展示了与现有基线相比的显著改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
