Add to ORKGFederated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a ha...
Federated learning is widely used to learn intelligent models from decentralized data. In federated ...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) enables multiple clients to collaboratively train a globally generalized mod...
In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especiall...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning (FL) supports distributed training of a global machine learning model across mult...
Is it possible to design an universal API for federated learning using which an ad-hoc group of data...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated Learning (FL) is a machine learning setting where many devices collaboratively train a mac...
Federated learning is widely used to learn intelligent models from decentralized data. In federated ...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) enables multiple clients to collaboratively train a globally generalized mod...
In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especiall...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning is a new scheme of distributed machine learning, which enables a large number of ...
Federated learning (FL) supports distributed training of a global machine learning model across mult...
Is it possible to design an universal API for federated learning using which an ad-hoc group of data...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated Learning (FL) is a machine learning setting where many devices collaboratively train a mac...
Federated learning is widely used to learn intelligent models from decentralized data. In federated ...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...