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Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e.g., sensors measuring pressure, motion, and other types of data).
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J. Chen and A. Zhang, “Fedmsplit: Correlation-adaptive federated multi-task learning across multimodal split networks,” in
2022
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L. Yuan, J. Andrews, H. Mu, A. Vakil, R. Ewing, E. Blasch, and J. Li, “Interpretable passive multi-modal sensor fusion for human identification and activity recognition,”
2022
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2022
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J. DelPreto, C. Liu, Y. Luo, M. Foshey, Y. Li, A. Torralba, W. Matusik, and D. Rus, “Actionsense: A multimodal dataset and recording framework for human activities using wearable sensors in a kitchen environment,”
2022
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L. Yuan, Y. Ma, L. Su, and Z. Wang, “Peer-to-peer federated continual learning for naturalistic driving action recognition,” in
2023
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P. Qi, D. Chiaro, and F. Piccialli, “FL-FD: Federated learning-based fall detection with multimodal data fusion,”
2023
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2023
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2023
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L. Yuan, H. Chen, R. Ewing, E. Blasch, and J. Li, “Three dimensional indoor positioning based on passive radio frequency signal strength distribution,”
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2023
Cited alongside, same era.
V. P. Chellapandi, A. Upadhyay, A. Hashemi, and S. H. Żak, “On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing,”
2023
Cited alongside, same era.
V. P. Chellapandi, L. Yuan, S. H. Zak, and Z. Wang, “A Survey of Federated Learning for Connected and Automated Vehicles,”
2023
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V. P. Chellapandi, L. Yuan, C. G. Brinton, S. H. Zak, and Z. Wang, “Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges,”
2023
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2023
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2023
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S. Yang, L. Yuan, and J. Li, “Extraction and denoising of human signature on radio frequency spectrums,” in
2023
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L. Yuan, L. Su, and Z. Wang, “Federated transfer-ordered-personalized learning for driver monitoring application,”
2023
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