[13] Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. Ditto: Fair and robust federated learning through personalization. International Conference on Machine Learning, 2021.
[14] Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen. Personalized federated learning with moreau envelopes. In Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020.
[15] Jaehoon Oh, SangMook Kim, and Se-Young Yun. FedBABU: Toward enhanced representation for federated image classification. In International Conference on Learning Representations, 2022.
[16] Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. Personalized federated learning using hypernetworks. In Proceedings of the 38th International Conference on Machine Learning, 2021.
[17] Benyuan Sun, Hongxing Huo, Yi Yang, and Bo Bai. Partialfed: Cross-domain personalized federated learning via partial initialization. In Advances in Neural Information Processing Systems, 2021.
[18] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. SCAFFOLD: Stochastic controlled averaging for federated learning. In Proceedings of the 37th International Conference on Machine Learning, 2020.
[19] Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, and Richard Vidal. Personalized federated learning through local memorization. International Conference on Machine Learning, 2022.
[20] Surat Teerapittayanon, Bradley McDanel, and H. T. Kung. Branchynet: Fast inference via early exiting from deep neural networks. arXiv preprint 1709.01686, 2017.
[21] Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama. Adaptive neural networks for efficient inference. In Proceedings of the 34th International Conference on Machine Learning, 2017.
[22] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Weinberger. Multiscale dense networks for resource efficient image classification. In International Conference on Learning Representations, 2018.
[23] Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E. Gonzalez. Skipnet: Learning dynamic routing in convolutional networks. In The European Conference on Computer Vision, 2018.
[24] Xin Dai, Xiangnan Kong, and Tian Guo. Epnet: Learning to exit with flexible multi-branch network. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2020.
[25] Lanlan Liu and Jia Deng. Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution. In Association for the Advancement of Artificial Intelligence, 2018.
[26] Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi. Neural speed reading via skim-RNN. In International Conference on Learning Representations, 2018.
[27] Jian Xu, Xinyi Tong, and Shao-Lun Huang. Personalized federated learning with feature alignment and classifier collaboration. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=SXZr8aDKia.
[28] Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, and Virginia Smith. Motley: Benchmarking heterogeneity and personalization in federated learning. arXiv preprint 2206.09262, 2022.
[29] Blake E Woodworth, Kumar Kshitij Patel, and Nati Srebro. Minibatch vs local sgd for heterogeneous distributed learning. In Advances in Neural Information Processing Systems, 2020.
[30] Stackoverflow. TensorFlow Federated Datasets. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets, 2023. [Online; accessed 09-Jan-2023].
[31] Shakespeare. TensorFlow Federated Datasets. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/shakespeare, 2023. [Online; accessed 09-Jan-2023].
[32] Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik. Emmist: Extending mnist to handwritten letters. In 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
[33] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images, 2009.
[34] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.