RW

8 records found

Federated Learning (FL) is a revolutionary approach to machine learning that enables collaborative model training among multiple parties without exposing sensitive data. Introduced by Google in 2016, FL taps into the wealth of data generated by edge devices while prioritizing ...

FEVERLESS

Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels

Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation ...
Federated Learning (FL) is a beneficial decentralized learning approach for preserving the privacy of local datasets of distributed agents. However, the distributed property of FL and untrustworthy data introducing the vulnerability to backdoor attacks. In this attack scenario, a ...

MUDGUARD

Taming Malicious Majorities in Federated Learning using Privacy-preserving Byzantine-robust Clustering

Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ ...

PIVODL

Privacy-Preserving Vertical Federated Learning Over Distributed Labels

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been ...
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due to their superior ability to learn representations for complex graph data. Due to privacy concerns an ...
Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsui ...

Your Smart Contracts Are Not Secure

Investigating Arbitrageurs and Oracle Manipulators in Ethereum

Smart contracts on Ethereum enable billions of dollars to be transacted in a decentralized, transparent and trustless environment. However, adversaries lie await in the Dark Forest, waiting to exploit any and all smart contract vulnerabilities in order to extract profits from uns ...