无数据持续学习的云-设备协作模型服务器
Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
AI总结：
FedDCL通过预训练扩散模型提取类特定原型，实现无数据持续学习，解决云-设备协作中模型异质性和知识对齐问题。
摘要：
云-设备协同计算的兴起使得智能服务能够在分布式边缘设备上交付，同时利用集中化的云资源。在这种范式中，联邦学习（FL）已成为在不将原始数据从边缘设备传输到云的关键使能器。然而，随着新数据的持续出现和模型多样性的增加，传统联邦学习面临重大挑战，包括固有的数据异质性、模型异质性和灾难性遗忘问题，以及新的知识对齐挑战。在本研究中，我们引入了FedDCL，一种新的框架，旨在在模型异质的联邦设置中实现无数据持续学习的服务器模型。我们利用预训练的扩散模型提取轻量级的类特定原型，这提供了三倍的无数据优势，使：（1）为当前任务生成合成数据以增强训练并对抗非IID数据分布；（2）无示例生成性重放以保留先前任务的知识；（3）从异质设备到云服务器的无数据动态知识转移。在各种数据集上的实验结果展示了FedDCL的有效性，展示了其在动态环境中的联邦云-设备协作的通用性和实用性。
英文摘要：
The rise of cloud-device collaborative computing has enabled intelligent services to be delivered across distributed edge devices while leveraging centralized cloud resources. In this paradigm, federated learning (FL) has become a key enabler for privacy-preserving model training without transferring raw data from edge devices to the cloud. However, with the continuous emergence of new data and increasing model diversity, traditional federated learning faces significant challenges, including inherent issues of data heterogeneity, model heterogeneity and catastrophic forgetting, along with new challenge of knowledge misalignment. In this study, we introduce FedDCL, a novel framework designed to enable data-free continual learning of the server model in a model-heterogeneous federated setting. We leverage pre-trained diffusion models to extract lightweight class-specific prototypes, which confer a threefold data-free advantage, enabling: (1) generation of synthetic data for the current task to augment training and counteract non-IID data distributions; (2) exemplar-free generative replay for retaining knowledge from previous tasks; and (3) data-free dynamic knowledge transfer from heterogeneous devices to the cloud server.Experimental results on various datasets demonstrate the effectiveness of FedDCL, showcasing its potential to enhance the generalizability and practical applicability of federated cloud-device collaboration in dynamic settings.
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。
