40 results on '"Ximeng LIU"'
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2. Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization.
3. FIGRET: Fine-Grained Robustness-Enhanced Traffic Engineering.
4. MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization.
5. MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation.
6. SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers.
7. When Evolutionary Computation Meets Privacy.
8. Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data.
9. A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective.
10. An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability.
11. Federated Online/Offline Remote Data Inspection for Distributed Edge Computing.
12. Efficient Routing Algorithm Design for Large DetNet.
13. SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation.
14. Federated Learning based on Defending Against Data Poisoning Attacks in IoT.
15. Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons.
16. Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization.
17. Generation Matrix: An Embeddable Matrix Representation for Hierarchical Trees.
18. SoK: Fully Homomorphic Encryption Accelerators.
19. Case-Aware Adversarial Training.
20. Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning.
21. Backdoor Defense with Machine Unlearning.
22. Enhance transferability of adversarial examples with model architecture.
23. MaskBlock: Transferable Adversarial Examples with Bayes Approach.
24. Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness.
25. The Component Diagnosability of General Networks.
26. Robust Single-step Adversarial Training with Regularizer.
27. When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System.
28. Too Expensive to Attack: A Joint Defense Framework to Mitigate Distributed Attacks for the Internet of Things Grid.
29. Too Expensive to Attack: Enlarge the Attack Expense through Joint Defense at the Edge.
30. VerifyTL: Secure and Verifiable Collaborative Transfer Learning.
31. Learn to Forget: User-Level Memorization Elimination in Federated Learning.
32. Cloud-based Federated Boosting for Mobile Crowdsensing.
33. Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning.
34. Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud.
35. Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata.
36. FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT.
37. Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing.
38. Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing.
39. Revocable Federated Learning: A Benchmark of Federated Forest.
40. Lightning-Fast and Privacy-Preserving Outsourced Computation in the Cloud.
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