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Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices.
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Geyer, R.C., Klein, T., Nabi, M.: Differentially Private Federated Learning: A Client Level Perspective. ArXiv e-prints (Dec 2017)
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2021
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Flores, M., Dayan, I., Roth, H., Zhong, A., Harouni, A., Gentili, A., Abidin, A., Liu, A., Costa, A., Wood, B., et al.: Federated learning used for predicting outcomes in sars-cov-2 patients. Research Square (2021)
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Wainakh, A., Müßig, T., Grube, T., Mühlhäuser, M.: Label leakage from gradients in distributed machine learning. In: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). pp. 1–4. IEEE (2021)
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Closest in time.