A survey of multimodal federated learning: background, applications, and perspectives
Abstract Multimodal Federated Learning (MMFL) is a novel machine learning technique that
enhances the capabilities of traditional Federated Learning (FL) to support collaborative …
enhances the capabilities of traditional Federated Learning (FL) to support collaborative …
Multimodal Federated Learning in AIoT Systems: Existing Solutions, Applications, and Challenges
C Anagnostopoulos, A Gkillas, C Mavrokefalidis… - IEEE …, 2024 - ieeexplore.ieee.org
The unprecedented technological advancements in Artificial Intelligence (AI) and the
Internet of Things (IoT) have given rise to ecosystems of intelligent, interconnected devices …
Internet of Things (IoT) have given rise to ecosystems of intelligent, interconnected devices …
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
In personalized Federated Learning (pFL), high data heterogeneity can cause significant
gradient divergence across devices, adversely affecting the learning process. This …
gradient divergence across devices, adversely affecting the learning process. This …
Cross-Modal Meta Consensus for Heterogeneous Federated Learning
In the evolving landscape of federated learning (FL), the integration of multimodal data
presents both unprecedented opportunities and significant challenges. Existing works fall …
presents both unprecedented opportunities and significant challenges. Existing works fall …
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Multimodal Large Language Models (MLLMs) have made significant advancements,
demonstrating powerful capabilities in processing and understanding multimodal data. Fine …
demonstrating powerful capabilities in processing and understanding multimodal data. Fine …
Pilot: Building the Federated Multimodal Instruction Tuning Framework
In this paper, we explore a novel federated multimodal instruction tuning task (FedMIT),
which is significant for collaboratively fine-tuning MLLMs on different types of multimodal …
which is significant for collaboratively fine-tuning MLLMs on different types of multimodal …
Trustworthy Transfer Learning: A Survey
J Wu, J He - arXiv preprint arXiv:2412.14116, 2024 - arxiv.org
Transfer learning aims to transfer knowledge or information from a source domain to a
relevant target domain. In this paper, we understand transfer learning from the perspectives …
relevant target domain. In this paper, we understand transfer learning from the perspectives …
FedMBridge: Bridgeable Multimodal Federated Learning
Multimodal Federated Learning (MFL) addresses the setup of multiple clients with diversified
modality types (eg image, text, video, and audio) working together to improve their local …
modality types (eg image, text, video, and audio) working together to improve their local …