6 citations · 8 across the 5 of their papers we have counts for
5 papers
Logits Poisoning Attack in Federated Distillation
Yuhan Tang, Zhiyuan Wu, Bo Gao +3
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cr…
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout
Jingjing Xue, Min Liu, Sheng Sun +3
Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are…
FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization
Xujing Li, Min Liu, Sheng Sun +3
In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent…
Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs
Shan Wang, Sheng Sun, Min Liu +2
Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in rec…
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Min Liu +2
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem…