Motivation
既然放养，只能自救，大家都是我的导师。
研究方向争取不乱串。隐私保护。
2022.5.26
1 Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles （2020 IEEE Trans on vehicular technology）
2022.5.27
2022.5.27
3 Shielding Collaborative Learning: Mitigating Poisoning Attacks Through Client-Side Detection （2021 TDSC）
2022.5.28
2022.5.29
5 FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping （2021 NDSS）
2022.5.29
6 FLOD: Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority （2021 ESORICS）
2022.5.29
7 Privacy Enhanced Federated Learning Against Poisoning Adversaries （2021 TIFS）
2022.5.29
8 ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning （2022 TIFS）
2022.5.31
9 Privacy-Preserving Federated Deep Learning With Irregular Users （2022 TDSC）
2022.5.31
2022.6.2
11 Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation （2022 arXiv）
2022.6.5
12 IMPROVING FEDERATED LEARNING FACE RECOGNITION VIA PRIVACY-AGNOSTIC CLUSTERS （2022 ICLR）
2022.6.10
13 PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing （2022 TIFS）
14 An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning (2022 WWW)
2022.6.12
15 DetectPMFL: Privacy-Preserving Momentum Federated Learning Considering Unreliable Industrial Agents （2022 TII）
16 NPMML: A Framework for Non-Interactive Privacy-Preserving Multi-Party Machine Learning （2021 TDSC）
2022.6.15
