Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

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

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine
learning that effectively deals with the privacy leakage problem by utilizing cryptographic …

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) allows a set of agents to collaboratively train a model without
sharing their potentially sensitive data. This makes FL suitable for privacy-preserving …

Federated learning (FL) is an emerging distributed machine learning framework for
collaborative model training with a network of clients (edge devices). FL offers default client …

Federated learning (FL) allows a server to learn a machine learning (ML) model across
multiple decentralized clients that privately store their own training data. In contrast with …

Federated learning (FL) was originally regarded as a framework for collaborative learning
among clients with data privacy protection through a coordinating server. In this paper, we …

Federated Learning (FL) is a promising approach to privacy-preserving machine learning.
However, recent works reveal that gradients can leak private data. Using trusted SGX …