Dec 20, 2022 · Abstract: Privacy protection and incentive mechanism are two fundamental problems in federated learning (FL), which aim at protecting the ...

Abstract—Privacy protection and incentive mechanism are two fundamental problems in federated learning (FL), which aim at protecting the privacy of data ...

In this paper, we have proposed a dual-privacy preserving and quality-aware incentive mechanism, called PrivAim, for federated learning, which uses differential ...

A dual-privacy preserving and quality-aware incentive mechanism, PrivAim, for federated learning that utilizes differential privacy to protect the local ...

To solve these problems, in this paper, we propose a dual- privacy preserving and quality-aware incentive mechanism, PrivAim, for federated learning.

Dec 20, 2022 · Privacy protection and incentive mechanism are two fundamental problems in federated learning (FL), which aim at protecting the privacy of ...

2023. TLDR. A dual-privacy preserving and quality-aware incentive mechanism, PrivAim, for federated learning that utilizes differential privacy to protect the ...

Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.

Jan 4, 2024 · PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning. Privacy protection and incentive mechanism ...

PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning ... quality-aware incentive mechanism, PrivAim, for federated ...