Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Although federated learning offers a level of privacy by aggregating user data without direct
access, it remains inherently vulnerable to various attacks, including poisoning attacks …

Federated Learning (FL) is a distributed learning paradigm that enables different parties to
train a model together for high quality and strong privacy protection. In this scenario …

Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …

Training a deep neural network requires substantial data and intensive computing
resources. Unaffordable price holds back many potential applications of deep learning …

Federated learning is vulnerable to poisoning attacks in which malicious clients poison the
global model via sending malicious model updates to the server. Existing defenses focus on …

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Over the past years, the increasingly severe data island problem has spawned an emerging
distributed deep learning framework—federated learning, in which the global model can be …

Federated learning (FL) enables multiple worker devices share local models trained on their
private data to collaboratively train a machine learning model. However, local models are …