元学习|【联邦元学习】读懂Per-FedAvg个性化联邦学习(Personalized Federated Learning: A Meta-Learning Approach)

论文:Fallah A, Mokhtari A, Ozdaglar A. Personalized Federated Learning: A Meta-Learning Approach[J]. arXiv pre-print server, 2020.
这篇文章提出了FedAvg的变体——Per-FedAvg,旨在解决联邦学习中的个性化问题。在联邦学习中结合元学习模型MAML,试图找出一个全局模型,使之在每个节点针对其自身的损失函数进行更新后均表现良好。
【元学习|【联邦元学习】读懂Per-FedAvg个性化联邦学习(Personalized Federated Learning: A Meta-Learning Approach)】【原创,转载需标明出处】论文解析(内含论文原文):https://ripe-heliotrope-6f4.notion.site/Personalized-Federated-Learning-A-Meta-Learning-Approach-207f759963fb4af7b06a4ca5223d0a31

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