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Found 8 papers, 3 papers with code
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
The implementation of FedRA is straightforward and can be seamlessly integrated into any transformer-based model without the need for further modification to the original model.
One-Shot Federated Learning with Classifier-Guided Diffusion Models
Leveraging the extensive knowledge stored in the pre-trained diffusion model, the synthetic datasets can assist us in surpassing the knowledge limitations of the client samples, resulting in aggregation models that even outperform the performance ceiling of centralized training in some cases, which is convincingly demonstrated in the sufficient quantification and visualization experiments conducted on three large-scale multi-domain image datasets.
Collaborative Chinese Text Recognition with Personalized Federated Learning
In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations.
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Specifically, we first extract prototypes from the labeled data on the server and send them to the clients.
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for multi-domain collaborative image classification with powerful foundation models, like CLIP.
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning
In this paper, we first analyze the generalization bound of the aggregation model produced from knowledge distillation for the client domains, and then describe two challenges, server-to-client discrepancy and client-to-client discrepancy, brought to the aggregation model by the domain discrepancies.
Cross-domain Federated Object Detection
Federated learning can enable multi-party collaborative learning without leaking client data.
One-shot Federated Learning without Server-side Training
Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection.
