[ABC+16] Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaojiang Zheng. Tensorflow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI'16, page 265-283, USA, 2016. USENIX Association.
[BHS20] Saar Barkai, Ido Hakimi, and Assaf Schuster. Gap-aware mitigation of gradient staleness. In International Conference on Learning Representations, 2020.
[BTM+20] Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, and Nicholas D. Lane. Flower: A friendly federated learning research framework, 2020.
$\left[\mathrm{CCA}^{+}21\right]$  Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, and Huzefa Rangwala. Fedat: A high-performance and communication-efficient federated learning system with asynchronous tiers. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '21, New York, NY, USA, 2021. Association for Computing Machinery.
$\left[\mathrm{CDW}^{+}18\right]$  Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings, 2018.
[CKMT18] Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, and Ameet Talwalkar. Expanding the reach of federated learning by reducing client resource requirements, 2018.
[CNSR20] Yujing Chen, Yue Ning, Martin Slawski, and Huzefa Rangwala. Asynchronous online federated learning for edge devices with non-iid data. In 2020 IEEE International Conference on Big Data (Big Data), pages 15-24, 2020.
$\left[\mathrm{CSP}^{+}21\right]$  Mingzhe Chen, Nir Shlezinger, H. Vincent Poor, Yonina C. Eldar, and Shuguang Cui. Communication-efficient federated learning. Proceedings of the National Academy of Sciences, 118(17):e2024789118, 2021.
[DDT21] Enmao Diao, Jie Ding, and Vahid Tarokh. Heterofl: Computation and communication efficient federated learning for heterogeneous clients. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021.
[DTS+22] Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, and Tao Zhang. Federated learning challenges and opportunities: An outlook, 2022.
[Goo] Google. Tensorflow lite | ml for mobile and edge devices. https://www.tensorflow.org/lite. Accessed: 2022-08-14.
[HAA20] Chaoyang He, Murali Annavaram, and Salman Avestimehr. Group knowledge transfer: Federated learning of large cnns at the edge. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 14068-14080. Curran Associates, Inc., 2020.
[HBGS19] Ido Hakimi, Saar Barkai, Moshe Gabel, and Assaf Schuster. Taming momentum in a distributed asynchronous environment, 2019.
$\left[\mathrm{HLA}^{+}21\right]$  Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Donald Lane. FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.