Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies such as Continual Learning. To enable research reproducibility, we propose a set of experimental best practices that precisely capture and emulate complex learning scenarios. Our framework, Freddie, is the first entirely configurable framework for Federated Continual Learning (FCL), and it can be seamlessly deployed on a large number of machines thanks to the use of Kubernetes and containerization. We demonstrate the effectiveness of Freddie on two use cases, (i) large-scale FL on CIFAR100 and (ii) heterogeneous task sequence on FCL, which highlight unaddressed performance challenges in FCL scenarios.

翻译：联邦学习系统在异构且持续演化的环境中运行，其性能面临严峻挑战。在实际部署场景中，客户端的学习任务亦可能随时间动态变化，这要求引入持续学习等方法论。为促进研究可重复性，我们提出了一套能够精确捕捉并模拟复杂学习场景的实验最佳实践方案。我们的框架Freddie是首个完全可配置的联邦持续学习框架，借助Kubernetes与容器化技术，可实现大规模机器集群的无缝部署。我们在两个用例中验证了Freddie的有效性：（i）基于CIFAR100的大规模联邦学习；（ii）异构任务序列下的联邦持续学习，这些实验揭示了联邦持续学习场景中尚未解决的性能挑战。