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Found 14 papers, 3 papers with code
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
These models often deliver suboptimal performance when relying solely on a single image-text contrastive learning objective, spotlighting the need for more effective training mechanisms that guarantee robust cross-modal feature alignment.
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground.
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training
The results demonstrate that LW-FedMML can compete with conventional end-to-end federated multimodal learning (FedMML) while significantly reducing the resource burden on FL clients.
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
With the proposed mechanisms, LW-FedSSL achieves a $3. 3 \times$ reduction in memory usage, $2. 1 \times$ fewer computational operations (FLOPs), and a $3. 2 \times$ lower communication cost while maintaining the same level of performance as its end-to-end training counterpart.
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.
Ranked #7 on
Image Classification
on EMNIST-Balanced
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).
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 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.
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.
