Sok: Secure aggregation based on cryptographic schemes for federated learning
M Mansouri, M Önen, WB Jaballah… - Proceedings on Privacy …, 2023 - petsymposium.org
Secure aggregation consists of computing the sum of data collected from multiple sources
without disclosing these individual inputs. Secure aggregation has been found useful for …
without disclosing these individual inputs. Secure aggregation has been found useful for …
Long-term privacy-preserving aggregation with user-dynamics for federated learning
Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced
federated learning (FL), which enables the server to obtain the sum of users' locally trained …
federated learning (FL), which enables the server to obtain the sum of users' locally trained …
The resource problem of using linear layer leakage attack in federated learning
Secure aggregation promises a heightened level of privacy in federated learning,
maintaining that a server only has access to a decrypted aggregate update. Within this …
maintaining that a server only has access to a decrypted aggregate update. Within this …
Privacy-preserving federated learning via functional encryption, revisited
Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm
that allows multiple users to collaboratively train an intermediate model by exchanging local …
that allows multiple users to collaboratively train an intermediate model by exchanging local …
LERNA: secure single-server aggregation via key-homomorphic masking
This paper introduces LERNA, a new framework for single-server secure aggregation. Our
protocols are tailored to the setting where multiple consecutive aggregation phases are …
protocols are tailored to the setting where multiple consecutive aggregation phases are …
Improving accelerated federated learning with compression and importance sampling
Federated Learning is a collaborative training framework that leverages heterogeneous data
distributed across a vast number of clients. Since it is practically infeasible to request and …
distributed across a vast number of clients. Since it is practically infeasible to request and …
Loki: Large-scale data reconstruction attack against federated learning through model manipulation
Federated learning was introduced to enable machine learning over large decentralized
datasets while promising privacy by eliminating the need for data sharing. Despite this, prior …
datasets while promising privacy by eliminating the need for data sharing. Despite this, prior …
SCALR: Communication-Efficient Secure Multi-Party Logistic Regression
Privacy-preserving coded computing is a popular framework for multiple data-owners to
jointly train machine learning models, with strong end-to-end information-theoretic privacy …
jointly train machine learning models, with strong end-to-end information-theoretic privacy …
ZooPFL: Exploring black-box foundation models for personalized federated learning
When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …
arise from various limitations in resources. In addition to typical limitations such as data …
Secure aggregation for clustered federated learning
Clustered federated learning is a popular paradigm to tackle data heterogeneity in federated
learning, by training personalized models for groups of users with similar data distributions …
learning, by training personalized models for groups of users with similar data distributions …