Federated Learning (FL) allows machine learning models to train locally on individual
mobile devices, synchronizing model updates via a shared server. This approach …

Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

Federated learning (FL) has emerged as a promising paradigm for enabling the
collaborative training of models without centralized access to the raw data on local devices …

Federated learning (FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …

Federated Learning (FL) is a distributed training paradigm that enables clients scattered
across the world to cooperatively learn a global model without divulging confidential data …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

Federated Learning (FL) is a practical approach to train deep learning models
collaboratively across user-end devices, protecting user privacy by retaining raw data on …

Federated Learning (FL) has emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …