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FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing.

, , , and . CoRR, (2023)

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Multi-Agent Deep Reinforcement Learning for Digital Twin over 6G Wireless Communication in the Metaverse., , and . INFOCOM Workshops, page 1-6. IEEE, (2023)Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions., and . ICCA, page 1-7. IEEE, (2023)Virtual Reality in Metaverse Over Wireless Networks with User-Centered Deep Reinforcement Learning., , and . ICC, page 6639-6644. IEEE, (2023)Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches., and . GLOBECOM, page 3336-3341. IEEE, (2023)User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks., , and . CoRR, (2023)Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach., , and . CoRR, (2023)Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation., , and . CoRR, (2024)FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing., , , and . CoRR, (2023)Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning., , and . ICC, page 316-322. IEEE, (2023)Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach., , and . WF-IoT, page 1-7. IEEE, (2022)