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Found 7 papers, 0 papers with code
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity.
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.
An Efficient Federated Learning Framework for Training Semantic Communication System
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy.
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.
Boosting Federated Learning Convergence with Prototype Regularization
As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data.
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
The existing single prototype-based strategy represents a class by using the mean of the feature space.
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data.
