[2] H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629, 2016.
[3] Jianyu Wang, Ananda Theertha Yu, and Gregory Wornell. Federated learning with matched averaging. arXiv preprint arXiv:2002.06440, 2020.
[4] Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. Qsgd: Communication-efficient sgd via gradient quantization and encoding. In Advances in Neural Information Processing Systems, pages 1709-1720, 2017.
[5] Yuzhu Mao, Zihao Zhao, Guangfeng Yan, Yang Liu, Tian Lan, Linqi Song, and Wenbo Ding. Communication-efficient federated learning with adaptive quantization. ACM Transactions on Intelligent Systems and Technology (TIST), 13(4):1-26, 2022.
[6] Sebastian U Stich. Local sgd converges fast and communicates little. arXiv preprint arXiv:1805.09767, 2018.
[7] Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar. Federated multi-task learning. In Advances in Neural Information Processing Systems, pages 4424-4434, 2017.
[8] Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. Robust and communication-efficient federated learning from non-iid data. In IEEE transactions on neural networks and learning systems, 2019.
[9] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497, 2020.
[10] Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Reinforcement learning, pages 5-32, 1992.
[11] Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, and Jürgen Schmidhuber. Parameter-exploring policy gradients. *Neural Networks*, 23(4):551-559, 2010.
[12] Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber. Natural evolution strategies. The Journal of Machine Learning Research, 15(1):949–980, 2014.
[13] Xin Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, 1999.
[14] Joel Lehman and Risto Miikkulainen. Neuroevolution. Scholarpedia, 8(6):30977, 2013.
[15] Sebastian Risi and Julian Togelius. Neuroevolution in games: State of the art and open challenges. CoRR, abs/1410.7326, 2014.
[16] Sebastian Risi and Julian Togelius. Neuroevolution in games: State of the art and open challenges. IEEE Transactions on Computational Intelligence and AI in Games, 9(1):25-41, 2015.
[17] Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, and Qiuyi Zhang. Gradientless descent: High-dimensional zeroth-order optimization. arXiv preprint arXiv:1911.06317, 2019.
[18] Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, and Sebastian Flennerhag. Discovering attention-based genetic algorithms via meta-black-box optimization. arXiv preprint arXiv:2304.03995, 2023.
[19] Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. Sparse binary compression: Towards distributed deep learning with minimal communication. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2019.
[20] Jinjin Xu, Wenli Du, Yaochu Jin, Wangli He, and Ran Cheng. Ternary compression for communication-efficient federated learning. IEEE Transactions on Neural Networks and Learning Systems, 2020.