Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 04007 | |
| Number of page(s) | 8 | |
| Section | Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20257804007 | |
| Published online | 08 September 2025 | |
- Li, T., et al.: Federated optimization in heterogeneous networks. MLSys (2020). [Google Scholar]
- Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492 (2016). [Google Scholar]
- Smith, S.L., Kindermans, P.-J., Le, Q.V.: Don’t Decay the Learning Rate, Increase the Batch Size. Proc. ICLR (2018). [Google Scholar]
- Ying, C., Kumar, S., Chen, D., Wang, T., Cheng, Y.: Image Classification at Supercomputer Scale. Proc. NeurIPS (2019). [Google Scholar]
- Chen, Y., Sun, X., Jin, Y.: Communication-Efficient Federated Learning with Adaptive Batch Sizes. Proc. ICML (2021). [Google Scholar]
- Zhao, M., Sinha, A., Dai, W., Yu, X., Liang, K.: Federated Learning with Non-IID Data via Local and Global Distillation. Proc. AAAI (2022). [Google Scholar]
- Sattler, F., Wiedemann, S., Müller, K.-R., Samek, W.: Robust and Communication-Efficient Federated Learning from Non-IID Data. IEEE Trans. Neural Netw. Learn. Syst., 31(9) (year not provided). [Google Scholar]
- Mohri, M., Sivek, G., Suresh, A.T.: Agnostic Federated Learning. Proc. ICML (2019). [Google Scholar]
- Karimireddy, S.P., et al.: SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML (2020). [Google Scholar]
- Chen, Y., et al.: Federated Learning with Adaptive Batches: A Dynamic Local Step Approach. IEEE transactions on neural networks and learning systems, (2022). [Google Scholar]
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