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Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy.
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Alistarh, D., Grubic, D., Li, J.Z., Tomioka, R., Vojnovic, M.: QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. p. 1707–1718. NIPS’17 (2017)
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McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics. pp. 1273–1282. PMLR (2017)
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Wangni, J., Wang, J., Liu, J., Zhang, T.: Gradient sparsification for communication-efficient distributed optimization. In: Proceedings of 32 nd
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Cited alongside, same era.
Mayer, R., Jacobsen, H.A.: Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools. ACM Computing Surveys 53
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Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R.R., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific reports 10
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Cited alongside, same era.
Tak, A., Cherkaoui, S.: Federated edge learning: Design issues and challenges. IEEE Network (2020)
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Later among the works it cites.
Xu, J., Du, W., Jin, Y., He, W., Cheng, R.: Ternary compression for communication-efficient federated learning. IEEE Transactions on Neural Networks and Learning Systems (2020)
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Zhou, Y., Ye, Q., Lv, J.C.: Communication-efficient federated learning with compensated overlap-fedavg. IEEE Transactions on Parallel and Distributed Systems (2021)
2021
Later among the works it cites.
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