Add to ORKGFederated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. Its excellent privacy security potential promotes a wide range of FL applications in Internet-of-Things (IoT), wireless networks, mobile devices, autonomous vehicles, and cloud medical treatment. However, the FL method suffers from poor model performance on non-i.i.d. data and excessive traffic volume. To this end, we propose a personalized FL algorithm using a hierarchical proximal mapping based on the moreau envelop, named sparse federated learning with hierarchical personalized models (sFedHP), which significantly improves the global model performance facing diverse data. A continuously differentiable approximated ...
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergen...
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Personalized federated learning is aimed at allowing numerous clients to train personalized models w...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
Since federated learning (FL) has been introduced as a decentralized learning technique with privacy...
Federated learning (FL) allows model training from local data by edge devices while preserving data ...
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 ...
The increasing size of data generated by smartphones and IoT devices motivated the development of Fe...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes...
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privac...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving...
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergen...
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Personalized federated learning is aimed at allowing numerous clients to train personalized models w...
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collect...
Since federated learning (FL) has been introduced as a decentralized learning technique with privacy...
Federated learning (FL) allows model training from local data by edge devices while preserving data ...
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 ...
The increasing size of data generated by smartphones and IoT devices motivated the development of Fe...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing...
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes...
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privac...
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent y...
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving...
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergen...
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Personalized federated learning is aimed at allowing numerous clients to train personalized models w...