1. Alam, S., Liu, L., Yan, M., Zhang, M.: FedRolex: model-heterogeneous federated learning with rolling sub-model extraction. In: NeurIPS, pp. 29677–29690 (2022)
2. Castiglia, T., Wang, S., Patterson, S.: Flexible vertical federated learning with heterogeneous parties. IEEE Trans. Neural Netw. Learn. Syst. (2023)
3. Ceballos, I., et al.: SplitNN-driven vertical partitioning. arXiv arXiv:2008.04137 (2020)
4. Cho, Y.J., Wang, J., Chirvolu, T., Joshi, G.: Communication-efficient and model-heterogeneous personalized federated learning via clustered knowledge transfer. IEEE J. Sel. Top. Signal Process. 17(1), 234–247 (2023)
5. Défossez, A., Bottou, L., Bach, F.R., Usunier, N.: A simple convergence proof of Adam and Adagrad. Trans. Mach. Learn. Res. (2022)