most citedTAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

60 citations · 62 across the 4 of their papers we have counts for

collaborators
Showing cs.CRShow all

3 papers · 1 filter

cs.CR2025

Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning

Zhiyong Jin, Runhua Xu, Chao Li +3

Gradient sparsification, while mitigating communication bottlenecks in Federated Learning (FL), fundamentally alters the geometric landscape of model updates. We reveal that the re…

cs.CR2025★ 2 cited

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning

Runhua Xu, Shiqi Gao, Chao Li +2

Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mech…

cs.CR2025★ 60 cited

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

Runhua Xu, Bo Li, Chao Li +3

Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. How…