Multimodal Federated Learning is a collaborative training process involving multiple clients, each with diverse modality settings and data, conducting learning tasks without disclosing their local r…
Multimodal federated learningMachine learningMultimodal Federated Learning (MMFL) is a novel machine learning technique that enhances the capabilities of traditional Federated Learning (FL) to support collaborative training of local models using data available in various modalities. With the generation and ...
Multimodal Federated Learning on IoT Data Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending
Section 2.1 discusses the challenges in security management related to the evolution of Federated Learning and its application in Healthcare. Section 2.2 overviews the evolution of state of the art in user authentication Federated learning based. Section 3 describes the Machine Learning-based algorithm ...
Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with Internet-of-Things (IoT) devices, local data on clients are ...
Transfer Learning methods help transfer knowledge from some devices to others. Federated Transfer Learning methods benefit both Federated Learning and Transfer Learning. This newly proposed Federated Transfer Learning framework aims at connecting data islands with privacy protection. Our construction is based...
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 het...
To address these challenges, we propose a Byzantine-robust multimodal federated learning framework specifically designed for intelligent connected vehicles. Specifically, we first design a novel multimodal fusion architecture that can effectively integrate various sensor data while preserving privacy. The archit...
This paper proposes a multimodal federated learning framework that enables multiple enterprises to utilize private domain data to collaboratively train large models for vertical domains, achieving intelligent services across scenarios. The authors discuss in-depth the strategic transformation of federated ...
Kline, Adrienne et al. 2022. ‘Multimodal Machine Learning in Precision Health: A Scoping Review’.npj Digital Medicine5(1): 171. Lin, Yi-Ming et al. 2023. ‘Federated Learning on Multimodal Data: A Comprehensive Survey’.Machine Intelligence Research20(4): 539–53. ...