FPPL：一种高效且非独立同分布鲁棒的联邦持续学习框架
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
AI总结：
本文提出FPPL框架，通过协同学习原型增强的轻量级提示，无需重放即可在非独立同分布的联邦环境中缓解灾难性遗忘，实现高效且鲁棒的持续学习。
摘要：
联邦持续学习（FCL）旨在去中心化的联邦学习环境中从序列数据流中学习，同时缓解经典持续学习中的灾难性遗忘问题。现有的FCL方法通常采用典型的重放机制，这可能导致隐私泄露或带来额外的繁重存储和计算负担。本文提出了一种高效且对非独立同分布（non-IID）鲁棒的联邦持续学习框架，称为联邦原型增强提示学习（FPPL）。FPPL无需重放即可协同学习由原型增强的轻量级提示。在客户端侧，采用融合函数充分利用任务特定提示中蕴含的知识以缓解灾难性遗忘。此外，从服务器聚合的全局原型通过对比学习用于获取统一表示，减轻非独立同分布导致的数据异质性影响。在服务器侧，利用本地上传的原型对分类器进行去偏，进一步缓解由非独立同分布和灾难性遗忘导致的性能下降。实证评估证明了FPPL的有效性，以高效设计实现了显著性能，且对多种非独立同分布程度保持鲁棒。代码获取地址：https://github.com/ycheoo/FPPL。
英文摘要：
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storage and computational burdens. In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed. The FPPL can collaboratively learn lightweight prompts augmented by prototypes without rehearsal. On the client side, a fusion function is employed to fully leverage the knowledge contained in task-specific prompts for alleviating catastrophic forgetting. Additionally, global prototypes aggregated from the server are used to obtain unified representation through contrastive learning, mitigating the impact of non-IID-derived data heterogeneity. On the server side, locally uploaded prototypes are utilized to perform debiasing on the classifier, further alleviating the performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate the effectiveness of FPPL, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. Code is available at: https://github.com/ycheoo/FPPL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
