As an emerging distributed machine learning paradigm, federated learning has been extensively used in the domain of cloud–edge computing to collaboratively train models without uploading their raw data. However, the existing federated learning methods make an effort to train a single optimal model that encompasses all participating clients. These methods may perform poorly on some clients due to variations in data distribution and limited data availability of clients. Moreover, assigning weights to clients merely based on the quantity of the client data neglects the inter-client correlation. In this paper, we propose a personalized federated learning framework with cross-client knowledge distillation called FedCD. FedCD is composed of a local model training strategy with cross-client co-personalized knowledge fusion and a global model weighted aggregation mechanism via peer correlation. In the local model training strategy, FedCD fuses similar personalized knowledge from all clients to guide the lcoal training of the client. In the global model weighted aggregation mechanism, the server assigns weights to clients based on their influence among clients. Extensive experiments conducted on various datasets demonstrate that FedCD significantly improves the test accuracy by approximately 0.18%–16.65% compared to the baseline methods.

中文翻译：

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通过跨客户端知识蒸馏的个性化联邦云边协作框架


作为一种新兴的分布式机器学习范式，联邦学习已广泛用于云边缘计算领域，无需上传原始数据即可协作训练模型。但是，现有的联邦学习方法努力训练一个包含所有参与客户端的单个最佳模型。由于数据分布的变化和客户端的数据可用性有限，这些方法在某些客户端上可能表现不佳。此外，仅根据客户端数据的数量为客户端分配权重会忽略客户端间相关性。在本文中，我们提出了一个具有跨客户端知识蒸馏功能的个性化联邦学习框架，称为 FedCD。FedCD 由具有跨客户端协同个性化知识融合的本地模型训练策略和通过对等关联的全局模型加权聚合机制组成。在本地模型训练策略中，FedCD 融合了来自所有客户的相似个性化知识，以指导客户的 lcoal 训练。在全局模型加权聚合机制中，服务器根据客户端在客户端中的影响力为客户端分配权重。在各种数据集上进行的广泛实验表明，与基线方法相比，FedCD 显著提高了约 0.18%–16.65% 的测试准确性。