论文标题
找到您的朋友：与正确合作者的个性化联合学习
Find Your Friends: Personalized Federated Learning with the Right Collaborators
论文作者
论文摘要
在传统的联邦学习环境中，中央服务器协调客户网络来培训一个全球模型。但是，由于数据异质性，全球模型可能会为许多客户服务。此外，可能没有一个可信赖的中央党可以协调客户以确保每个人都可以从他人那里受益的中央党。为了解决这些问题，我们提出了一个新颖的分散框架Federico，每个客户都可以从其他客户那里学习到最佳的本地数据分发。基于期望最大化，费德里科（Federico）估计了每个客户数据上其他参与者模型的实用程序，以便每个人都可以选择合适的合作者进行学习。结果，我们的算法在多个基准数据集上优于其他联合，个性化和/或分散的方法，这是唯一比仅使用本地数据培训更好的方法。
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address these concerns, we present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants' models on each client's data so that everyone can select the right collaborators for learning. As a result, our algorithm outperforms other federated, personalized, and/or decentralized approaches on several benchmark datasets, being the only approach that consistently performs better than training with local data only.
