Multi-modalFederated learningCo-attentionFederated Learning (FL) is a machine learning setting that separates data and protects user privacy. Clients learn global models together without data interaction. However, due to the lack of high-quality labeled data collected from the real world, most of ...
In this paper, we aim to solve a novel challenge in multi-modal federated learning (MFL) – modality missing – the clients may lose part of the modalities in their local data sets. To tackle the problems, we propose a novel multi-modal federated learning method, Fed erated M ulti-modal...
Here comes the federated learning, whose main idea is to create a global classifier without accessing the users’ local data. Therefore, we have developed a federated learning framework for real-time emotion state classification using multi-modal physiological data streams from wearable sensors, called...
2024-04-29 FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition Yuxuan Yan et.al. 2404.18848 null 2024-04-29 It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Comments Petter Mæhlum et.al. 2404.18832 null 202...
Serving as a pivotal technology within the realm of privacy-preserving computation, federated learning employs a mechanism wherein a central server trains a shared global model while keeping sensitive data stored locally within each participating institution, and thus ensure the preservation of privacy ...
Federated learning inspired privacy sensitive emotion recognition based on multi-modal physiological sensors It uses Multi-layer Perceptron (MLP) as a base model for classifying complex emotions in three dimensions: Valence, Arousal, and Dominance (VAD). The ... N Gahlan,D Sethia - 《Cluster Comp...
Panda R, Chen C F R, Fan Q, et al. Adamml: Adaptive multi-modal learning for efficient video recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 7576-7585. 在视频理解任务中,多种模态的输入数据往往会携带大量的冗余信息,从而影响计算效率和实时...
[ICASSP 2024] Prompt-based Personalized Federated Learning for Medical Visual Question Answering [pdf] [arXiv 2024] RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning [pdf] [arXiv 2024] Design as Desired: Utilizing Visual Question Answering for Multimo...
Federated learning is an efcient machine learning method that can expand between multiple parameters or multiple computing nodes. It has been applied successfully in the nancial industry and cross-industry cooperation. In this paper, a novel algorithm to disease diagnosis model based on ...
Privacy-preserving approaches, e.g., via federated learning, can allow safe sharing of data or models across cloud providers [105]. However, the creation of interoperable systems that follow the requirement for the representation of clinical knowledge is important for the broad adoption of such ...