Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of …
L Sun, J Qian, X Chen - arXiv preprint arXiv:2007.15789, 2020 - arxiv.org
Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection …
W Lu, X Hu, J Wang, X Xie - arXiv preprint arXiv:2302.13485, 2023 - arxiv.org
Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …
X Shang, Y Lu, G Huang, H Wang - arXiv preprint arXiv:2204.13399, 2022 - arxiv.org
Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL …
We consider the problem of Federated Learning (FL) where numerous decentralized computational nodes collaborate with each other to train a centralized machine learning …
L Sun, L Lyu - arXiv preprint arXiv:2009.05537, 2020 - arxiv.org
Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead …
R Hu, Y Gong, Y Guo - arXiv preprint arXiv:2008.01558, 2020 - arxiv.org
Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not …
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods …
Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients' private data from being exposed to adversaries. Nevertheless, private …