16 citations · 44 across the 6 of their papers we have counts for
7 papers
Federated Two Stage Decoupling With Adaptive Personalization Layers
Hangyu Zhu, Yuxiang Fan, Zhenping Xie
Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequenc…
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Hangyu Zhu, Rui Wang, Yaochu Jin +1
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing th…
Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu +1
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than…
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning
Hangyu Zhu, Rui Wang, Yaochu Jin +2
Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a t…
From Federated Learning to Federated Neural Architecture Search: A Survey
Hangyu Zhu, Haoyu Zhang, Yaochu Jin
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of prim…
Real-time Federated Evolutionary Neural Architecture Search
Hangyu Zhu, Yaochu Jin
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is t…
Multi-objective Evolutionary Federated Learning
Hangyu Zhu, Yaochu Jin
Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them col…