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Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data.
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Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions
Ramu, S.P., Boopalan, P., Pham, Q.V., Maddikunta, P.K.R., Huynh-The, T., Alazab, M., Nguyen, T.T., Gadekallu, T.R., 2022 · 2022
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A unified framework for multi-modal federated learning
Xiong, B., Yang, X., Qi, F., Xu, C., 2022 · 2022
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Zhao, Y., Barnaghi, P., Haddadi, H., 2022 · 2022
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Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., Li, K., 2022 · 2022
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Federated learning for smart cities: A comprehensive survey
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Multimodal federated learning via contrastive representation ensemble
Yu, Q., Liu, Y., Wang, Y., Xu, K., Liu, J., 2023 · 2023
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The opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tröster, G., Millán, J.d.R., Roggen, D., 2013 · 2042
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