Add to ORKGOne-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, \eg a public dataset is required, clients' models are homogeneous, and additional data/model information need to be uploaded. To overcome these issues, we propose a novel two-stage \textbf{D}ata-fre\textbf{E} o\textbf{N}e-\textbf{S}hot federated l\textbf{E}arning (DENSE) framework, which trains the global model by a data generation stage and a model distillation stage. DENSE is a practical one-shot FL method that can be applied in reality due to the following advanta...
In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively...
Federated learning (FL) is a technique for distributed machine learning that enables the use of silo...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularl...
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training ...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data ...
Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to cont...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively...
Federated learning (FL) is a technique for distributed machine learning that enables the use of silo...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularl...
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training ...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data ...
Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to cont...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively...
Federated learning (FL) is a technique for distributed machine learning that enables the use of silo...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...