20 citations · 36 across the 6 of their papers we have counts for
6 papers
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Thomas Schneider, Ajith Suresh, Hossein Yalame
In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic e…
Attesting Distributional Properties of Training Data for Machine Learning
Vasisht Duddu, Anudeep Das, Nora Khayata +3
The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness. Several jurisdictions are preparing ML regulatory frameworks. One such co…
WW-FL: Secure and Private Large-Scale Federated Learning
Felix Marx, Thomas Schneider, Ajith Suresh +3
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. However, recent…
ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas +7
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. Unfortunat…
High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings
Christopher Harth-Kitzerow, Ajith Suresh, Yongqin Wang +3
In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most…
FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
Thien Duc Nguyen, Phillip Rieger, Huili Chen +10
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive loc…