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Found 4 papers, 1 papers with code
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning
To address this problem, we develop a novel feature protection scheme against the reconstruction attack that effectively misleads the search to some pre-specified random values.
Pisces: Efficient Federated Learning via Guided Asynchronous Training
Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration.
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data.
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning
Homomorphic encryption (HE) is a promising privacy-preserving technique for cross-silo federated learning (FL), where organizations perform collaborative model training on decentralized data.
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