1 citations · 1 across the 3 of their papers we have counts for
4 papers
FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Shouxu Lin, Zimeng Pan, Yuhang Yao +3
Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing t…
Evaluating Selective Encryption Against Gradient Inversion Attacks
Jiajun Gu, Yuhang Yao, Shuaiqi Wang +1
Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local…
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
Siddharth Ambekar, Yuhang Yao, Ryan Li +1
Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of…
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Yuhang Yao, Yuan Li, Xinyi Fan +7
Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks,…