In cross-silo federated learning (FL), the data among clients are usually statistically
heterogeneous (aka not independent and identically distributed, non-IID) due to diversified …

Federated learning (FL) is recently surging as a promising decentralized deep learning (DL)
framework that enables DL-based approaches trained collaboratively across clients without …

Conventional federated learning (FL) trains one global model for a federation of clients with
decentralized data, reducing the privacy risk of centralized training. However, the distribution …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …

Federated learning (FL) is an emerging technology that enables the training of machine
learning models from multiple clients while keeping the data distributed and private. Based …

Federated learning (FL) is a machine learning technique that enables participants to
collaboratively train high-quality models without exchanging their private data. Participants …

Federated learning (FL) is a machine learning paradigm for data silos and privacy
protection, which aims to organize multiple clients for training global machine learning …

Federated learning is an emerging technique to collaboratively train machine learning
models over multiple clients without exposing private data but suffers from heterogeneous …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated learning (FL) has been widely studied as an emerging privacy-preserving
machine learning paradigm for achieving multi-party collaborative model training on …