Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to guaranteeing consistent performance. Existing work only addresses these problems separately, which neglects the fact that the root cause behind both forms of performance deterioration is connected. We propose a unified analysis framework for building a controlled test environment for Client Drift -- by perturbing a defined ratio of clients -- and Catastrophic Forgetting -- by shifting all clients with a particular strength. Our framework further leverages this new combined analysis by generating a 3D landscape of the combined performance impact from both. We demonstrate that the performance drop through Client Drift, caused by a certain share of shifted clients, is correlated to the drop from Catastrophic Forgetting resulting from a corresponding shift strength. Correlation tests between both problems for Computer Vision (CelebA) and Medical Imaging (PESO) support this new perspective, with an average Pearson rank correlation coefficient of over 0.94. Our framework's novel ability of combined spatio-temporal shift analysis allows us to investigate how both forms of distribution shift behave in mixed scenarios, opening a new pathway for better generalization. We show that a combination of moderate Client Drift and Catastrophic Forgetting can even improve the performance of the resulting model (causing a "Generalization Bump") compared to when only one of the shifts occurs individually. We apply a simple and commonly used method from Continual Learning in the federated setting and observe this phenomenon to be reoccurring, leveraging the ability of our framework to analyze existing and novel methods for Federated and Continual Learning.

联邦学习和持续学习已经被认为是在动态环境中利用深度学习进行稳健和隐私感知的潜在范式。然而，客户端变化和灾难性遗忘是确保一致性性能的根本障碍。我们提出了一个统一的分析框架来构建一个受控的测试环境，用于客户端变化和灾难性遗忘的研究。通过生成一个包括两者的性能影响的三维图像，我们展示了分布变化在混合场景中的行为，为更好的泛化打开了新的途径。我们还发现适度的客户端变化和灾难性遗忘的组合甚至可以提高模型的性能。我们的研究能力允许分析联邦学习和持续学习的现有和新方法。

动态学习中联合探索客户漂移和灾难性遗忘