Add to ORKGFederated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper, we propose 'ProtoFL', Prototypical Representation Distillation based unsupervised Federated Learning to enhance the representation power of a global model and reduce round communication costs. Additionally, we introduce a local one-class classifier based on normalizing flows to improve performance with limited data. Our study represents the first investigation of using FL to improve one-class classification performance. We conduct extensive exper...
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train ...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Making deep learning models efficient at inferring nowadays requires training with an extensive numb...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the cent...
A possible approach to address the increasing security and privacy concerns is federated learning (F...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning is a distributed framework where a server computes a global model by aggregating ...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a ce...
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Machine learning models benefit from large and diverse training datasets. However, it is difficult f...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train ...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Making deep learning models efficient at inferring nowadays requires training with an extensive numb...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated learning (FL) is an important paradigm for training global models from decentralized data ...
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the cent...
A possible approach to address the increasing security and privacy concerns is federated learning (F...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased re...
Federated learning is a distributed framework where a server computes a global model by aggregating ...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a ce...
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Machine learning models benefit from large and diverse training datasets. However, it is difficult f...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train ...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Making deep learning models efficient at inferring nowadays requires training with an extensive numb...