Liwei Che

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Figure 3. A diagram of the screening process.

Aug 9, 2023 | researchgate.net | Liwei Che |Jiaqi Wang |Yao Zhou |Fenglong Ma

Source publicationFederated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non-ide... Context 1... shown in Figure 3, the literature search and selection process was conducted via two stages.
Figure 1. Illustration of traditional unimodal FL v.s. multimodal FL.

Aug 9, 2023 | researchgate.net | Liwei Che |Jiaqi Wang |Yao Zhou |Fenglong Ma

Source publicationFederated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non-ide... Context 1...
Figure 5. Illustration of multimodal federated transfer learning and...

Aug 9, 2023 | researchgate.net | Liwei Che |Jiaqi Wang |Yao Zhou |Fenglong Ma

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Multimodal Federated Learning: A Survey

Aug 6, 2023 | mdpi.com | Jiaqi Wang |Yao Zhou |Fenglong Ma |Liwei Che

Abstract:Federated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non-identical distribution (non-IID) caused by a data distribution imbalance related to data labels and data amount.
Multimodal Federated Learning: A Survey

Aug 6, 2023 | researchgate.net | Liwei Che |Jiaqi Wang |Yao Zhou |Fenglong Ma

Federated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non-identical distribution (non-IID) caused by a data distribution imbalance related to data labels and data amount.