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 …

Leveraging federated learning (FL) to enable cross-domain privacy-sensitive data mining
represents a vital breakthrough to accomplish privacy-preserving learning. However …

As a novel distributed learning framework for protecting personal data privacy, federated
learning has attained widespread attentions through sharing gradients among users without …

Fixing Issues and Achieving Maliciously Secure Verifiable Aggregation in “VeriFL:
Communication-Efficient and Fast Verifiable Page 1 Fixing Issues and Achieving Maliciously …

Secure aggregation protocols are implemented in federated learning to protect the local
models of the participating users so that the server does not obtain any information beyond …

Secure aggregation enables federated learning (FL) to perform collaborative training of
clients from local gradient updates without exposing raw data. However, existing secure …

Federated learning has been a paradigm for privacy-preserving machine learning, but
recently gradient leakage attacks threaten privacy in federated learning. Secure aggregation …

Verifiable secure aggregation (VSA) is a critical procedure in federated learning (FL), where
secure aggregation achieves local gradients aggregation while data confidentiality is …

Secure aggregation protects the local models of the users in federated learning, by not
allowing the server to obtain any information beyond the aggregate model at each iteration …

Secure aggregation becomes a major solution to providing privacy for federated learning.
Secure aggregation for mobile devices typically relies on Shamir secret sharing (SSS) to …