[74] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
[75] Steven T Piantadosi. Zipf's word frequency law in natural language: A critical review and future directions. Psychonomic bulletin & review, 21:1112-1130, 2014.
[76] George Kingsley Zipf. Human behavior and the principle of least effort: An introduction to human ecology. Ravenio Books, 2016.
[77] Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. In ICCV, December 2015.
[78] Jack Bandy and Nicholas Vincent. Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus. arXiv Preprint, 2021.
[79] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv Preprint, 2016.
[80] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL, pages 4171–4186, 2019.
[81] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273–1282. PMLR, 20–22 Apr 2017. URL https://proceedings.mlr.press/v54/mcmahan17a.html.
[82] Zachary Charles and Keith Rush. Iterated vector fields and conservatism, with applications to federated learning. In  $COLT$ , pages 130-147, 2022.
[83] Yihan Jiang, Jakub Konečny, Keith Rush, and Sreeram Kannan. Improving Federated Learning Personalization via Model Agnostic Meta Learning. arXiv Preprint, 2019.
[84] Alireza Fallah, Aryan Mokhtari, and Asuman E. Ozdaglar. Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. In NeurIPS, 2020.
[85] Zachary Charles and Jakub Konečný. Convergence and accuracy trade-offs in federated learning and meta-learning. In AISTATS, pages 2575–2583, 2021.
[86] Milad Khademi Nori, Sangseok Yun, and Il-Min Kim. Fast Federated Learning by Balancing Communication Trade-Offs. IEEE Transactions on Communications, 69(8):5168-5182, 2021.
[87] Gary Cheng, Karan Chadha, and John Duchi. Fine-tuning is Fine in Federated Learning. arXiv Preprint, 3, 2021.
[88] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. In NeurIPS, 2022.
[89] Alex Nichol and John Schulman. On First-Order Meta-Learning Algorithms. arXiv Preprint, 2018.
[90] Bernard Koch, Emily Denton, Alex Hanna, and Jacob G. Foster. Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research. In NeurIPS Datasets and Benchmarks, 2021.