Federated learning (FL) enables collaborative and privacy-preserving training of machine
learning (ML) models on federated data. However, the barriers to using FL are still high …

Federated Learning (FL) has emerged as a promising paradigm to train machine learning
models collaboratively while preserving data privacy. However, its widespread adoption …

Federated learning (FL) has emerged as a prominent paradigm enabling collaborative
model training without transmitting local data, thereby safeguarding data privacy. However …

Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables
distributed clients to collaboratively learn a shared global model while keeping the data on …

Federated learning is a promising machine learning paradigm that enables participating
clients to train models collaboratively with privacy restrictions. However, one of the most …

Federated Learning epitomizes a sophisticated distributed machine learning methodology,
enabling collaborative neural network model training across multiple entities without …

Federated learning (FL) enables collaborative model training while keeping data
decentralized. However, system heterogeneity and statistical differences in decentralized …

Federated machine learning promises to overcome the input privacy challenge in machine
learning. By iteratively updating a model on private clients and aggregating these local …

Federated learning (FL) is an emerging distributed machine learning paradigm, which has
shown great potential in collaborative learning with privacy preservation. However, FL …

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to
coordinate multiple decentralized clients for training a common model without exposing their …