Journal/Magazine Papers

  1. Z. Wang, Y. Zhu, D. Wang, Z. Han, “Towards Fair and Scalable Trial Assignment in Federated Bandits: A Shapley Value Approach“, to appear in IEEE Transactions on Big Data, 2024
  2. S. Cen, M. Zhang, Y. Zhu, J. Liu, “AdaDSR: Adaptive Configuration Optimization for Neural Enhanced Video Analytics Streaming“, to appear in IEEE Internet of Things Journal, 2023
  3. Y. Kang, Y. Zhu, D. Wang, Z. Han, T. Basar, “Joint Server Selection and Handover Design for Satellite-Based Federated Learning Using Mean-field Evolutionary Approach”, to appear in IEEE Transactions on Network Science and Engineering, 2023.
  4. Q. Pan, H. Cao, Y. Zhu, J. Liu, B. Li, “Contextual Client Selection for Efficient Federated Learning over Edge Devices“, to appear in IEEE Transactions on Mobile Computing, 2023. (CCF-A)
  5. S. Shi, Y. Guo, D. Wang, Y. Zhu, Z. Han, “Distributionally Robust Federated Learning for Network Traffic Classification with Noisy Labels“, to appear in IEEE Transactions on Mobile Computing, 2023. (CCF-A)
  6. W. Gong, L. Cao, Y. Zhu, F. Zuo, X. He, H. Zhou, “Federated Inverse Reinforcement Learning for Smart ICUs with Differential Privacy“, in IEEE Internet of Things Journal, 2023
  7. C. Wu, Y. Zhu, R. Zhang, Y. Chen, F. Wang, S. Cui, “FedAB: Truthful Federated Learning with Auction-based Combinatorial Multi-Armed Bandit“, in IEEE Internet of Things Journal, 2023
  8. B. Zhu, S. Lin, Y. Zhu, X. Wang, “Collaborative Hyperspectral Image Processing using Satellite Edge Computing“, in IEEE Transactions on Mobile Computing, 2023. (CCF-A)
  9. S. Shi, C. Hu, D. Wang, Y. Zhu, Z. Han, “Federated HD Map Updating through Overlapping Coalition Formation Game“, in IEEE Transactions on Mobile Computing, 2023. (CCF-A)
  10. D. Chen, Y. Zhu, D. Wang, H. Wang, J. Xie, X. Zhang, Z. Han, “Love of Variety based Latency Analysis for High Definition Map Updating: Age of Information and Distributional Robust Perspectives“, in IEEE Transactions on Intelligent Vehicles, 2022
  11. Z. Wang, Y. Zhu, D. Wang, Z. Han, “Secure Trajectory Publication in Untrusted Environments: A Federated Analytics Approach“, in IEEE Transactions on Mobile Computing, 2022. [Code] (CCF-A)
  12. Z. Wang, Y. Zhu, D. Wang, Z. Han, “Federated Analytics Informed Distributed Industrial IoT Learning with Non-IID Data“, in IEEE Transactions on Network Science and Engineering, 2022. [Code]
  13. T. Wang, S. Chen, Y. Zhu, A. Tang, and X. Wang, “LinkSlice: Fine-grained Network Slice Enforcement Based on Deep Reinforcement Learning“, in IEEE Journal on Selected Areas in Communications, 2022. (CCF-A)
  14. M. Zhang, Y. Zhu, J. Liu, F. Wang, F. Wang, “CharmSeeker: Automated Pipeline Configuration for Serverless Video Processing“, in IEEE/ACM Transaction on Networking, 2022. (CCF-A)
  15. J. Zhang, S. Chen, X. Wang, Y. Zhu, “Dynamic Reservation of Edge Servers via Deep Reinforcement Learning for Connected Vehicles“, in IEEE Transactions on Mobile Computing, 2021. (CCF-A)
  16. D. Wang, S. Shi, Y. Zhu, Z. Han. “Federated Analytics: Opportunities and Challenges“,  in IEEE Network, 2021.
  17. M. Zhang, F. Wang, Y. Zhu, J. Liu, B. Li. “Serverless Empowered Video Analytics for Ubiquitous Networked Cameras“,  in IEEE Network, 2021.
  18. S. Shi, C. Hu, D. Wang, Y. Zhu, Z. Han, “Federated Anomaly Analytics for Local Model Poisoning Attack“, in IEEE Journal on Selected Areas in Communications, 2021. (CCF-A)
  19. D. Chen, D. Wang, Z. Han, Y. Zhu, “Digital Twin for Federated Analytics Using A Bayesian Approach“, in IEEE Internet of Things Journal, 2021
  20. F. Wang, C. Zhang, F. Wang, J. Liu, Y. Zhu, H. Pang, L. Sun, “DeepCast: Towards Personalized QoE for Edge-Assisted Crowdcast With Deep Reinforcement Learning“, in IEEE/ACM Transaction on Networking, 2020 (CCF-A)
  21. F. Wang, Y. Zhu, F. Wang, J. Liu, X. Ma, X. Fan.  “Car4Pac: Last Mile Parcel Delivery through Intelligent Car Trip Sharing“, in IEEE Transactions on Intelligent Transportation Systems, 2019.
  22. Y. Zhu, Q. He, J. Liu, B. Li, Y. Hu. “When Crowd Meets Big Video Data: Cloud-Edge Collaborative Transcoding for Personal Livecast“, in IEEE Transactions on Network Science and Engineering, 2018.
  23. Y. Zhu, S. Fu, J. Liu, Y. Cui. “Truthful Online Auction Towards Maximized Instance Utilization in the Cloud“, in IEEE/ACM Transaction on Networking, 2018. (CCF-A)

Conference Papers

  1. Z. Li, M. Zhang, Y. Zhu, “OAVS: Efficient Online Learning of Streaming Policies for Drone-sourced Live Video Analytics“, to appear in IEEE/ACM IWQoS 2024
  2. Y. Liu, Z. Wang, Y. Zhu, C. Chen, “DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service“, in IEEE INFOCOM 2024 (CCF-A)
  3. Z. Wang, Y. Zhu, D. Wang, Z. Han, “Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications“, in IEEE INFOCOM 2024 (CCF-A)
  4. J. Huang, Y. Zhu, “SpaceMeta: Global-Scale Massive Multi-User Virtual Interaction over LEO Satellite Constellations“,  in IEEE Satellite 2023
  5. M. Zhang, J. Li, J. Shi, Y. Zhu, L. Zhang, H. Wang, “ITSVA: Toward 6G-Enabled Vision Analytics over Integrated Terrestrial-Satellite Network“, in IEEE Satellite 2023
  6. D. Song, C. Zhang, Y. Zhu, J. Liu, “LiGo: A Low Cost Cross-Platform Deployment Framework Empowers Video Processing Application“, in ACM NOSSDAV 2023
  7. Q. Pan, Y. Zhu, L. Chu, “Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices“, to appear in IEEE ICDE 2023 (CCF-A)
  8. M. Zhang, Y. Zhu, L. Shen, F. Wang, J. Liu, “OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos“, to appear in IEEE INFOCOM 2023 (CCF-A)
  9. S. Fu, D. Reimer, S. Dong, Y. Zhu, S. Ratnasamy, “Comverse: A Federative-by-Design Platform for Community Computing“, CoRR abs/2308.15219, 2023
  10. C. Wu, Y. Zhu, F. Wang, “DSFL: Decentralized Satellite Federated Learning for Energy-Aware LEO Constellation Computing“, in IEEE Satellite 2022  (Best Student Paper Award)
  11. Y. Zhu, W. Bao, D. Wang, J. Liu. “A Stackelberg Queuing Model and Analysis for the Emerging Connection-based Pricing in IoT Markets“,  in IEEE MASS, 2022
  12. K. Chen, Y. Zhu, Z. Han, X. Wang. “Adaptive Cross-Camera Video Analytics at the Edge“, in IEEE MASS, 2022
  13. K. Chen, Y. Zhu, Y. Kang, Z. Han. “Few-Shot Correlation Estimation for Cross-Camera Video Analytics: A Mean-Field Game Approach“, in IEEE PIMRC, Native-AI in wireless networks workshop, 2022
  14. C. Tang, K. Ouyang, Z. Wang, Y. Zhu, W. Ji, Y. Wang, W. Zhu. “Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance“, in ECCV 2022
  15. Y. Lu, Y. Zhu, Z. Wang. “Personalized 360-Degree Video Streaming: A Meta-Learning Approach“, in ACM Multimedia, 2022 (CCF-A)
  16. C. Tang, H. Zhai, K. Ouyang, Z. Wang, Y. Zhu, W. Zhu. “Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach“,  in ACM Multimedia, 2022 (CCF-A)
  17. Q. Pan, Y. Zhu. “FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy“, in ACM SIGKDD, 2022 (CCF-A)
  18. X. Yuan, M. Wu, Z. Wang, Y. Zhu, M. Ma, J. Guo, Z. Zhang, W. Zhu. “Understanding 5G Performance for Real-world Services: a Content Provider’s Perspective“,  in ACM SIGCOMM, 2022 (CCF-A)
  19. S. Shi, C. Hu, D. Wang, Y. Zhu, Z Han. “Distributionally Robust Federated Learning for Differentially Private Data“, in IEEE ICDCS, 2022
  20. H. Cao, Q. Pan, Y. Zhu, J. Liu.”Birds of a Feather Help: Context-aware Client Selection for Federated Learning“,  in International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022
  21. Z. Wang, Y. Zhu, D. Wang, Z. Han. “FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining“, in IEEE INFOCOM, 2022. [Code] (CCF-A)
  22. S. Xie, Y. Xue, Y. Zhu, Z. Wang. “Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach“,  in IEEE INFOCOM, 2022 (CCF-A)
  23. M. Zhang, F. Wang, Y. Zhu, J. Liu, Z. Wang. “Towards Cloud-Edge Collaborative Online Video Analytics with Fine-Grained Serverless Pipelines“, in ACM MMSys, 2021.
  24. Z. Wang, Y. Zhu, D. Wang, Z. Han. “FedACS: Federated Skewness Analytics in Heterogeneous Decentralized Data Environments“, in IEEE/ACM IWQoS, 2021
  25. J. Zhang, S. Chen, X. Wang, Y. Zhu. “DeepReserve: Dynamic Edge Server Reservation for Connected Vehicles with Deep Reinforcement Learning“, in IEEE INFOCOM, 2021 (CCF-A)
  26. M. Zhang, Y. Zhu, C. Zhang, J. Liu. “Video processing with serverless computing: a measurement study“, in ACM NOSSDAV, 2019
  27. F. Wang, C. Zhang, J. Liu, Y. Zhu, H. Pang, L. Sun. “Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE“, in IEEE INFOCOM, 2019 (CCF-A)
  28. Y. Huang, Y. Zhu, X. Fan, X. Ma, F. Wang, J. Liu, Z. Wang, Y. Cui. “Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning“, in IEEE ICCCN, 2018
  29. F. Wang, Y. Zhu, F. Wang, J. Liu. “Ridesharing as a Service: Exploring Crowdsourced Connected Vehicle Information for Intelligent Package Delivery“, in IEEE/ACM IWQoS, 2018
  30. Y. Zhu, J. Liu, Z. Wang, C. Zhang. “When Cloud Meets Uncertain Crowd: An Auction Approach for Crowdsourced Livecast Transcoding“, in ACM Multimedia, 2017 (CCF-A)
  31. S. Fu, Y. Zhu, J. Liu. “HARV: Harnessing hybrid virtualization to improve instance (re) usage in public cloud“, in IEEE/ACM IWQoS, 2017
  32. Y. Zhu, S. Fu, J. Liu, Y. Cui. “Truthful Online Auction for Cloud Instance Subletting“, in IEEE ICDCS, 2017
  33. Y. Zhu, J. Jiang, B. Li, B. Li. “Rado: A Randomized Auction Approach for Data Offloading via D2D Communication“, in IEEE MASS, 2015
  34. J. Jiang, Y. Zhu, B. Li, B. Li. “Rally: Device-to-device content sharing in LTE networks as a game“, IEEE MASS, 2015

Demos and Posters

  1. Y. Zhu, B. Zhu, C. Chen, X. Fan. “Towards Efficient Compound Large Language Model System Serving in the Wild“, in IEEE/ACM IWQoS 2024
  2. D. Song, Y. Zhu, C. Zhang, J. Liu. “Trueno: A Cross-Platform Machine Learning Model Serving Framework in Heterogeneous Edge Systems“, in IEEE INFOCOM 2022
  3. Y. Zhu, J. Liu. “C2: Procuring uncertain freelancers for interactive live video transcoding“, in IEEE/ACM IWQoS 2017

Book Chapter

  1. D. Chen, D. Wang, Y. Zhu, and Z. Han, “Digital Twin for Federated Analytics Applications,” Handbook of Digital Twins, CRC Press