Pysyft: A library for easy federated learning
PySyft is an open-source multi-language library enabling secure and private machine
learning by wrapping and extending popular deep learning frameworks such as PyTorch in …
learning by wrapping and extending popular deep learning frameworks such as PyTorch in …
{PrivateFL}: Accurate, differentially private federated learning via personalized data transformation
Federated learning (FL) enables multiple clients to collaboratively train a model with the
coordination of a central server. Although FL improves data privacy via keeping each client's …
coordination of a central server. Although FL improves data privacy via keeping each client's …
A generic framework for privacy preserving deep learning
We detail a new framework for privacy preserving deep learning and discuss its assets. The
framework puts a premium on ownership and secure processing of data and introduces a …
framework puts a premium on ownership and secure processing of data and introduces a …
A review of privacy-preserving federated learning for the Internet-of-Things
Abstract The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is
attributable to human activities and behavior. Collecting personal data and executing …
attributable to human activities and behavior. Collecting personal data and executing …
Privacyfl: A simulator for privacy-preserving and secure federated learning
V Mugunthan, A Peraire-Bueno, L Kagal - Proceedings of the 29th ACM …, 2020 - dl.acm.org
Federated learning is a technique that enables distributed clients to collaboratively learn a
shared machine learning model without sharing their training data. This reduces data …
shared machine learning model without sharing their training data. This reduces data …
Hybridalpha: An efficient approach for privacy-preserving federated learning
Federated learning has emerged as a promising approach for collaborative and privacy-
preserving learning. Participants in a federated learning process cooperatively train a model …
preserving learning. Participants in a federated learning process cooperatively train a model …
A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
Fedv: Privacy-preserving federated learning over vertically partitioned data
Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …
learning (ML) models among multiple parties to keep their data private and only model …
LDP-Fed: Federated learning with local differential privacy
This paper presents LDP-Fed, a novel federated learning system with a formal privacy
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …
LDP-FL: Practical private aggregation in federated learning with local differential privacy
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 …
concerns in many areas. Federated learning is a popular approach for privacy protection …