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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neural Information Processing Systems 3557--3568.  Alireza Fallah Aryan Mokhtari and Asuman Ozdaglar. 2020. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neural Information Processing Systems 3557--3568."},{"key":"e_1_3_2_2_12_1","volume-title":"Advances in Neural Information Processing Systems","author":"Ghosh Avishek","year":"2020","unstructured":"Avishek Ghosh , Jichan Chung , Dong Yin , and Kannan Ramchandran . 2020 . An efficient framework for clustered federated learning . Advances in Neural Information Processing Systems , 19586--19597. Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020. An efficient framework for clustered federated learning. 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Advances in Neural Information Processing Systems 15625--15638.  Md Aamir Raihan and Tor Aamodt. 2020. Sparse weight activation training. Advances in Neural Information Processing Systems 15625--15638."},{"key":"e_1_3_2_2_37_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, Virtual Event, 8253--8265","author":"Rothchild Daniel","year":"2020","unstructured":"Daniel Rothchild , Ashwinee Panda , Enayat Ullah , Nikita Ivkin , Ion Stoica , Vladimir Braverman , Joseph Gonzalez , and Raman Arora . 2020 . FetchSGD: communication-efficient federated learning with sketching . In Proceedings of the International Conference on Machine Learning. PMLR, Virtual Event, 8253--8265 . Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora. 2020. FetchSGD: communication-efficient federated learning with sketching. In Proceedings of the International Conference on Machine Learning. 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