Efficiency optimization techniques in privacy-preserving federated learning with homomorphic encryption: A brief survey
Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …
Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
A robust privacy-preserving federated learning model against model poisoning attacks
A Yazdinejad, A Dehghantanha… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Although federated learning offers a level of privacy by aggregating user data without direct
access, it remains inherently vulnerable to various attacks, including poisoning attacks …
access, it remains inherently vulnerable to various attacks, including poisoning attacks …
SAFELearn: Secure aggregation for private federated learning
Federated learning (FL) is an emerging distributed machine learning paradigm which
addresses critical data privacy issues in machine learning by enabling clients, using an …
addresses critical data privacy issues in machine learning by enabling clients, using an …
Privacy-preserving aggregation in federated learning: A survey
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
LSFL: A lightweight and secure federated learning scheme for edge computing
Nowadays, many edge computing service providers expect to leverage the computational
power and data of edge nodes to improve their models without transmitting data. Federated …
power and data of edge nodes to improve their models without transmitting data. Federated …
Eluding secure aggregation in federated learning via model inconsistency
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its
inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of …
inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of …
Efficient dropout-resilient aggregation for privacy-preserving machine learning
Machine learning (ML) has been widely recognized as an enabler of the global trend of
digital transformation. With the increasing adoption of data-hungry machine learning …
digital transformation. With the increasing adoption of data-hungry machine learning …
Efficient bootstrapping for approximate homomorphic encryption with non-sparse keys
We present a bootstrapping procedure for the full-RNS variant of the approximate
homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared …
homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared …
Piranha: A {GPU} platform for secure computation
Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …
learning (ML). However, secure training of large-scale ML models currently requires a …