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Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems., , and . CoRR, (2019)A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices., , , and . ICSOC, volume 12571 of Lecture Notes in Computer Science, page 319-333. Springer, (2020)Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning., , and . IWCMC, page 524-529. IEEE, (2023)Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving., , and . IWCMC, page 318-323. IEEE, (2022)VirtualGAN: Reducing Mode Collapse in Generative Adversarial Networks Using Virtual Mapping., , and . IJCNN, page 1-6. IEEE, (2021)How to Distribute the Detection Load among Virtual Machines to Maximize the Detection of Distributed Attacks in the Cloud?, , , and . SCC, page 316-323. IEEE Computer Society, (2016)Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems., , , and . IEEE Commun. Surv. Tutorials, 23 (2): 1342-1397 (2021)Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game., , , , and . SCC, page 443-449. IEEE, (2021)A Game-Theoretic Approach for Distributed Attack Mitigation in Intelligent Transportation Systems., , and . NOMS, page 1-6. IEEE, (2020)Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning., , , and . MobiWIS, volume 12814 of Lecture Notes in Computer Science, page 121-134. Springer, (2021)