Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

TL;DR

This paper introduces a federated class incremental learning framework that uses a server-trained generative model to synthesize past data, effectively mitigating catastrophic forgetting without storing or sharing private data.

Contribution

It proposes a novel data-free generative approach for federated continual learning, enhancing privacy and reducing data storage needs while improving performance.

Findings

Significant performance gains on CIFAR-100 dataset.

Effective mitigation of catastrophic forgetting in federated settings.

Reduces privacy risks by avoiding data sharing or storage.

Abstract

Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines.