As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized approach of training artificial intelligence …

Federated learning (FL), as a privacy-enhanced distributed machine learning paradigm, has
achieved tremendous success in solving the data silo problem. However, data heterogeneity …

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that
enables multiple clients to collaboratively train statistical models without disclosing raw …

Federated learning (FL) is a promising decentralized machine learning approach that
enables multiple distributed clients to train a model jointly while keeping their data private …

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm
that allows collaborative learning with data privacy protection, personalized FL (pFL) has …

Federated learning has quickly gained popularity with its promises of increased user privacy
and efficiency. Previous works have shown that federated gradient updates contain …

Federated learning (FL) has been used as a promising approach to breaking the dilemma
between the data privacy and the learning from large collections of distributed data. Without …

As an emerging technique, Federated Learning (FL) can jointly train a global model with the
data remaining locally, which effectively solves the problem of data privacy protection …

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

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