K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0811-6_26
Reference34 articles.
1. Chai, Z., et al.: TiFL: a tier-based federated learning system. In: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (2020). https://doi.org/10.1145/3369583.3392686
2. Chai, Z., Chen, Y., Zhao, L., Cheng, Y., Rangwala, H.: FedAT: a communication-efficient federated learning method with asynchronous tiers under non-IID data (2020)
3. Chen, M., Mao, B., Ma, T.: FedSA: a staleness-aware asynchronous federated learning algorithm with non-IID data. Futur. Gener. Comput. Syst. 120, 1–12 (2021)
4. Chen, Y., Sun, X., Jin, Y.: Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Trans. Neural Netw. Learn. Syst. 4229–4238 (2019). https://doi.org/10.1109/tnnls.2019.2953131
5. Dai, W., Zhou, Y., Dong, N., Zhang, H., Xing, E.: Toward understanding the impact of staleness in distributed machine learning (2018)
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