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论文：《Improving federated learning personalization via model agnostic meta learning》Citation：Y. Jiang, J. Konecny, K. Rush, and S. Kannan, “Improving federated learning personalization via model agnostic meta learning,” arXiv preprint arXiv:1909.12488, 201

论文《Learning to learn without forgetting by maximizing transfer and minimizing interference》中提出了“将经验重放与元学习相结合“的增量学习方法：Meta-Experience Replay (MER)。这里整理了一下MER的算法流程和代码实现。论文解析可以戳这里：​​​​​​论文解析：Learning to learn without forgetting by maximizing transfer and mi

论文：J. Rajasegaran, S. Khan, M. Hayat, F. S. Khan, M. Shah, itaml: An incremental task-agnostic meta-learning approach, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13588–13597.在CVPR2020上发表的一篇结合元学习和增量学习的

论文：Zheng W, Yan L, Gou C, et al. Federated Meta-Learning for Fraudulent Credit Card Detection[C], Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special Track on AI in FinTech. Pages 4654-4660. 2020: 4654-4660.【

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。【原创，转载需标明出处】论文解析（内含论文原

论文：Chen F , Luo M , Dong Z , et al. Federated Meta-Learning with Fast Convergence and Efficient Communication[J]. arXiv, 2018.华为的一篇结合联邦学习和元学习的论文：在联邦框架下，基于MAML或Meta-SGD构建FedMeta模型。【原创，转载需标明出处】论文解析（内含论文原文、代码链接）：https://ripe-heliotrope-6f4.notion.site/Fed

论文:M. Reimer, I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, G. Tesauro, Learning to learn without forgetting by maximizing transfer and minimizing interference, in: ICLR, 2019.提出了Meta-Experience Replay (MER) 模型，将经验重放与基于优化的元学习相结合，基于Reptile元学习模型，以最大限度地转移和.