Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Federated learning (FL) provides a high efficient decentralized machine learning framework,
where the training data remains distributed at remote clients in a network. Though FL …

Federated learning (FL) enables a great deal of distributed independent participants to
collaborate in training without sharing data. Malicious adversary can poison the local model …

Federated learning (FL) naturally faces the problem of data heterogeneity in real-world
scenarios, but this is often overlooked by studies on FL security and privacy. On the one …

Federated learning (FL) is a widely used distributed machine learning framework. However,
recent studies have shown its susceptibility to poisoning membership inference attacks …

Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data
privacy, security, access rights and access to heterogeneous information issues by training a …

Federated learning is inherently vulnerable to poisoning attacks, since no training samples
will be released to and checked by trustworthy authority. Poisoning attacks are widely …

The federated learning framework builds a deep learning model collaboratively by a group
of connected devices via only sharing local parameter updates to the central parameter …

Federated learning is a decentralized machine learning technique that aggregates partial
models trained by a set of clients on their own private data to obtain a global model. This …

Federated learning (FL) is an emerging machine learning paradigm. With FL, distributed
data owners aggregate their model updates to train a shared deep neural network …