Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions
<p dir="ltr">The rapidly expanding demand for intelligent wireless applications and the Internet of Things (IoT) requires advanced system designs to handle multimodal data effectively while ensuring user privacy and data security. Traditional machine learning (ML) models rely on cent...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| _version_ | 1864513534587568128 |
|---|---|
| author | Mumin Adam (22466626) |
| author2 | Abdullatif Albaseer (16904607) Uthman Baroudi (22466629) Mohamed Abdallah (3073191) |
| author2_role | author author author |
| author_facet | Mumin Adam (22466626) Abdullatif Albaseer (16904607) Uthman Baroudi (22466629) Mohamed Abdallah (3073191) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mumin Adam (22466626) Abdullatif Albaseer (16904607) Uthman Baroudi (22466629) Mohamed Abdallah (3073191) |
| dc.date.none.fl_str_mv | 2025-04-14T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcoms.2025.3554537 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Survey_of_Multimodal_Federated_Learning_Exploring_Data_Integration_Challenges_and_Future_Directions/30405547 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning Multimodal FL data fusion cross-modal multimodal federated transformer learning , multimodal FL communication intelligent IoT applications Data models Surveys Transformers Data privacy Computational modeling Internet of Things Accuracy Scalability Federated learning Distributed databases |
| dc.title.none.fl_str_mv | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The rapidly expanding demand for intelligent wireless applications and the Internet of Things (IoT) requires advanced system designs to handle multimodal data effectively while ensuring user privacy and data security. Traditional machine learning (ML) models rely on centralized architectures, which, while powerful, often present significant privacy risks due to the centralization of sensitive data. Federated Learning (FL) is a promising decentralized alternative for addressing these issues. However, FL predominantly handles unimodal data, which limits its applicability in environments where devices collect and process various data types such as text, images, and sensor output. To address this limitation, Multimodal FL (MMFL) integrates multiple data modalities, enabling a richer and more holistic understanding of data. In this survey, we explore the challenges and advancements in MMFL, including data representation, fusion techniques, and cross-modal learning strategies. We present a comprehensive taxonomy of MMFL, outlining critical challenges such as modality imbalance, fusion complexity, and security concerns. Additionally, we highlight the role of transformers in MMFL by leveraging their powerful attention mechanisms to process multimodal data in a federated setting. Finally, we discuss various applications of MMFL, including healthcare, human activity recognition, and emotion recognition, and propose future research directions for improving the scalability and robustness of MMFL systems in real-world scenarios.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2025.3554537" target="_blank">https://dx.doi.org/10.1109/ojcoms.2025.3554537</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8be4f66522a093dd639d847ea36a208e |
| identifier_str_mv | 10.1109/ojcoms.2025.3554537 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30405547 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future DirectionsMumin Adam (22466626)Abdullatif Albaseer (16904607)Uthman Baroudi (22466629)Mohamed Abdallah (3073191)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceData management and data scienceMachine learningMultimodal FLdata fusioncross-modalmultimodal federated transformer learning ,multimodal FL communication intelligent IoT applicationsData modelsSurveysTransformersData privacyComputational modelingInternet of ThingsAccuracyScalabilityFederated learningDistributed databases<p dir="ltr">The rapidly expanding demand for intelligent wireless applications and the Internet of Things (IoT) requires advanced system designs to handle multimodal data effectively while ensuring user privacy and data security. Traditional machine learning (ML) models rely on centralized architectures, which, while powerful, often present significant privacy risks due to the centralization of sensitive data. Federated Learning (FL) is a promising decentralized alternative for addressing these issues. However, FL predominantly handles unimodal data, which limits its applicability in environments where devices collect and process various data types such as text, images, and sensor output. To address this limitation, Multimodal FL (MMFL) integrates multiple data modalities, enabling a richer and more holistic understanding of data. In this survey, we explore the challenges and advancements in MMFL, including data representation, fusion techniques, and cross-modal learning strategies. We present a comprehensive taxonomy of MMFL, outlining critical challenges such as modality imbalance, fusion complexity, and security concerns. Additionally, we highlight the role of transformers in MMFL by leveraging their powerful attention mechanisms to process multimodal data in a federated setting. Finally, we discuss various applications of MMFL, including healthcare, human activity recognition, and emotion recognition, and propose future research directions for improving the scalability and robustness of MMFL systems in real-world scenarios.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2025.3554537" target="_blank">https://dx.doi.org/10.1109/ojcoms.2025.3554537</a></p>2025-04-14T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcoms.2025.3554537https://figshare.com/articles/journal_contribution/Survey_of_Multimodal_Federated_Learning_Exploring_Data_Integration_Challenges_and_Future_Directions/30405547CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304055472025-04-14T12:00:00Z |
| spellingShingle | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions Mumin Adam (22466626) Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning Multimodal FL data fusion cross-modal multimodal federated transformer learning , multimodal FL communication intelligent IoT applications Data models Surveys Transformers Data privacy Computational modeling Internet of Things Accuracy Scalability Federated learning Distributed databases |
| status_str | publishedVersion |
| title | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| title_full | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| title_fullStr | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| title_full_unstemmed | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| title_short | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| title_sort | Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence Data management and data science Machine learning Multimodal FL data fusion cross-modal multimodal federated transformer learning , multimodal FL communication intelligent IoT applications Data models Surveys Transformers Data privacy Computational modeling Internet of Things Accuracy Scalability Federated learning Distributed databases |