Federated learning (FL) has gained widespread popularity in a variety of industries due to its
ability to locally train models on devices while preserving privacy. However, FL systems are …

We revisit the problem of designing scalable protocols for private statistics and private
federated learning when each device holds its private data. Our first contribution is to …

Secure aggregation is commonly used in federated learning (FL) to alleviate privacy
concerns related to the central aggregator seeing all parameter updates in the clear …

Several membership inference (MI) attacks have been proposed to audit a target DNN.
Given a set of subjects, MI attacks tell which subjects the target DNN has seen during …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

The promise of least-privilege learning--to find feature representations that are useful for a
learning task but prevent inference of any sensitive information unrelated to this task--is …

Federated learning is an emerging distributed learning paradigm that allows multiple users
to collaboratively train a joint machine learning model without having to share their private …

Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in
federated learning, affording the server access only to the aggregate of model updates while …

In this paper, we explore the realm of federated learning (FL), a distributed machine learning
(ML) paradigm, and propose a novel approach that leverages the robustness of blockchain …

In federated learning, secure aggregation (SA) protocols like Flamingo (S\&P'23) and
LERNA (ASIACRYPT'23) have achieved efficient multi-round SA in the malicious model …