The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and predefined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://github.com/LTTM/FedSpace.

翻译：标准类增量持续学习设定假设一组任务以固定且预定义的顺序依次出现。这在联邦学习环境中并不现实，在该环境中每个客户端独立异步工作，以完全互不相关的时间段和顺序获取不同任务的数据。我们提出了一种新颖的联邦学习设定（AFCL），其中多个任务的持续学习在每个客户端以不同顺序和异步时隙进行。我们使用基于原型的学习、表示损失、分形预训练和修改的聚合策略来解决这一新任务。我们的方法称为FedSpace，在CIFAR-100数据集上使用3种不同的联邦数据划分（分别涉及50、100和500个客户端）的结果表明，该方法有效解决了该任务。代码和联邦数据划分可在https://github.com/LTTM/FedSpace获取。