Federated Learning (FL) enables data owners to train a shared global model without
sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due …

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning
paradigm. To motivate data owners to contribute towards FL, research on FL incentive …

Federated learning (FL) is a promising approach in distributed learning keeping privacy.
However, during the training pipeline of FL, slow or incapable clients (ie, stragglers) slow …

Federated learning (FL) is a machine learning paradigm where multiple clients collaborate
to optimize a single global model using their private data. The global model is maintained by …

Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm.
Nonetheless, the substantial distribution shifts among clients pose a considerable challenge …

Federated learning (FL) is increasingly deployed among multiple clients to train a shared
model over decentralized data. To address privacy concerns, FL systems need to safeguard …

Federated learning (FL) enables multiple clients to collaboratively learn a shared model
without sharing their individual data. Concerns about utility, privacy, and training efficiency in …

Federated learning enables multiple parties to collaboratively learn a model without
exchanging their data. While most existing federated learning algorithms need many rounds …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

This work proposes FedLAP-DP, a novel privacy-preserving approach for federated
learning. Unlike previous linear point-wise gradient-sharing schemes, such as FedAvg, our …