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 this https URL

本文提出了一种名为 FedSpace 的解决异步时间段下面向多任务连续学习问题的方法，它基于原型学习、表示损失、分形预训练和改进聚合策略，在 Cifar-100 数据集上得到了有效的应用。