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Found 11 papers, 2 papers with code
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
In this paper, we propose Multimodal Federated Cross Prototype Learning (MFCPL), a novel approach for MFL under severely missing modalities by conducting the complete prototypes to provide diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism.
Attention on Personalized Clinical Decision Support System: Federated Learning Approach
Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning
Many recent studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw training data distributed across edge devices.
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV).
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy.
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
We conduct experiments on various FL scenarios, and our findings demonstrate that federated diffusion models have great potential to deliver vision services to privacy-sensitive domains.
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL.
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
Federated Learning based Energy Demand Prediction with Clustered Aggregation
In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.
Federated Learning with Intermediate Representation Regularization
Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models.
