IEEE INFOCOM 2024
Opening, Awards, and Keynote
Networking Research in the Age of AI/ML: More Science, Less Hubris
Dr. Walter Willinger (Chief Scientist at NIKSUN, Inc.)
Speaker Dr. Walter Willinger, Chief Scientist at NIKSUN, Inc.
Coffee Break
A-1: Network Privacy
X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics
Dou Feng (Huazhong University of Science and Technology, China); Lin Wang (Paderborn University, Germany); Shutong Chen (Guangxi University, China); Lingching Tung and Fangming Liu (Huazhong University of Science and Technology, China)
Speaker Shutong Chen (Guangxi University)
Shutong Chen is an Assistant Professor at Guangxi University in China. She received Ph.D. degree from Huazhong University of Science and Technology. Her research interests include edge computing and green computing.
Privacy-Preserving Data Evaluation via Functional Encryption, Revisited
Xinyuan Qian and Hongwei Li (University of Electronic Science and Technology of China, China); Guowen Xu (City University of Hong Kong, China); Haoyong Wang (University of Electronic Science and Technology of China, China); Tianwei Zhang (Nanyang Technological University, Singapore); Xianhao Chen (University of Hong Kong, China); Yuguang Fang (City University of Hong Kong, Hong Kong)
Speaker Xinyuan Qian (University of Electronic Science and Technology of China)
Xinyuan Qian is currently a Ph.D. student at the School of Computer Science and Engineering, University of Electronic Science and Technology of China, and a visiting researcher in Prof. Fang Yuguang's lab at City University of Hong Kong. His research interests include IBE, ABE, FE, applied cryptography, and privacy-preserving machine learning.
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
Yu Liu, Zibo Wang, Yifei Zhu and Chen Chen (Shanghai Jiao Tong University, China)
Speaker Yu Liu (Shanghai Jiao Tong Univ.)
Optimal Locally Private Data Stream Analytics
Shaowei Wang, Yun Peng and Kongyang Chen (Guangzhou University, China); Wei Yang (University of Science and Technology of China, China)
We present an optimal, streamable mechanism for local differentially private sparse vector estimation. The mechanism enables a range of online analytics on streaming binary vectors, including multi-dimensional binary, categorical, or set-valued data. By leveraging the negative correlation of occurrence events in the sparse vector, we attain an optimal error rate under local privacy constraints, only requiring streamable computations during the input's data-dependent phase. Through experiments with both synthetic and real-world datasets, our proposals have been shown to reduce error rates by 40% to 60% compared to SOTA approaches.
Speaker Shaowei Wang (Guangzhou University)
Session Chair
Batyr Charyyev (University of Nevada Reno, USA)
B-1: Radio Access Networks
Det-RAN: Data-Driven Cross-Layer Real-Time Attack Detection in 5G Open RANs
Alessio Scalingi (IMDEA Networks, Spain); Salvatore D'Oro, Francesco Restuccia and Tommaso Melodia (Northeastern University, USA); Domenico Giustiniano (IMDEA Networks Institute, Spain)
Speaker
Providing UE-level QoS Support by Joint Scheduling and Orchestration for 5G vRAN
Jiamei Lv, Yi Gao, Zhi Ding, Yuxiang Lin and Xinyun You (Zhejiang University, China); Guang Yang (Alibaba Group, China); Wei Dong (Zhejiang University, China)
Speaker Jiamei Lv (Zhejiang University)
Jiamei Lv is currently a Researcher at School of Software Technology, Zhejiang University. She received her Ph.D. degree from the College of Computer Science, Zhejiang University in 2023. Her research intresets includes Internet of Things, edge computing, and 5G.
ORANUS: Latency-tailored Orchestration via Stochastic Network Calculus in 6G O-RAN
Oscar Adamuz-Hinojosa (University of Granada, Spain); Lanfranco Zanzi (NEC Laboratories Europe, Germany); Vincenzo Sciancalepore (NEC Laboratories Europe GmbH, Germany); Andres Garcia-Saavedra (NEC Labs Europe, Germany); Xavier Costa-Perez (ICREA and i2cat & NEC Laboratories Europe, Spain)
Speaker
OREO: O-RAN intElligence Orchestration of xApp-based network services
Federico Mungari and Corrado Puligheddu (Politecnico di Torino, Italy); Andres Garcia-Saavedra (NEC Labs Europe, Germany); Carla Fabiana Chiasserini (Politecnico di Torino & CNIT, IEIIT-CNR, Italy)
Speaker
Session Chair
Ning Lu (Queen's University, Canada)
C-1: UAV networking
Online Radio Environment Map Creation via UAV Vision for Aerial Networks
Neil C Matson (Georgia Institute of Technology, USA); Karthikeyan Sundaresan (Georgia Tech, USA)
Speaker
A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Zemin Sun, Geng Sun, Long He and Fang Mei (Jilin University, China); Shuang Liang (Northeast Normal University, China); Yanheng Liu (Jilin University, China)
Speaker
An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing
Long He, Geng Sun and Zemin Sun (Jilin University, China); Pengfei Wang (Dalian University of Technology, China); Jiahui Li (Jilin University, China); Shuang Liang (Northeast Normal University, China); Dusit Niyato (Nanyang Technological University, Singapore)
Speaker
Near-Optimal UAV Deployment for Delay-Bounded Data Collection in IoT Networks
Shu-Wei Chang (National Yang Ming Chiao Tung University, Taiwan); Jian-Jhih Kuo (National Chung Cheng University, Taiwan); Mong-Jen Kao (National Yang-Ming Chiao-Tung University, Taiwan); Bo-Zhong Chen and Qian-Jing Wang (National Chung Cheng University, Taiwan)
Speaker
Session Chair
Enrico Natalizio (University of Lorraine/Loria, France)
D-1: Federated Learning 1
AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation
Tengxi Xia and Ju Ren (Tsinghua University, China); Rao Wei, Zu Qin, Wang Wenjie and Chen Shuai (Mei Tuan, China); Yaoxue Zhang (Tsinghua University, China)
Speaker Tengxi Xia (Tsinghua University)
Hello everyone, my name is Xia Tengxi. I completed my undergraduate degree in Software Engineering at Harbin University of Science and Technology. I am currently pursuing a doctoral degree in the Computer Science Department at Tsinghua University.
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Zhi Yuan Wu and Sheng Sun (Institute of Computing Technology, Chinese Academy of Sciences, China); Yuwei Wang (Institute of Computing Technology Chinese Academy of Sciences, China); Min Liu (Institute of Computing Technology, Chinese Academy of Sciences, China); Bo Gao (Beijing Jiaotong University, China); Quyang Pan, Tianliu He and Xuefeng Jiang (Institute of Computing Technology, China)
Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53% accuracy gains and remarkable improvements in convergence rate.
Speaker Zhiyuan Wu (Institute of Computing Technology, Chinese Academy of Sciences)
Zhiyuan Wu currently is a research assistant with the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). He has contributed several technical papers to top-tier conferences and journals as the first author in the fields of computer architecture, computer networks, and intelligent systems, including IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Mobile Computing (TMC), IEEE International Conference on Computer Communications (INFOCOM), and ACM Transactions on Intelligent Systems and Technology (TIST). He has served as a technical program committee member or a reviewer for over 10 conferences and journals, and was invited to serve as a session chair for the International Conference on Computer Technology and Information Science (CTIS). He is a member of IEEE, ACM, the China Computer Federation (CCF), and is granted the President Special Prize of ICT, CAS. His research interests include federated learning, mobile edge computing, and distributed systems.
BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency
Jing Qiao (Shandong University, China); Zuyuan Zhang (George Washington University, USA); Sheng Yue (Tsinghua University, China); Yuan Yuan (Shandong University, China); Zhipeng Cai (Georgia State University, USA); Xiao Zhang (Shandong University, China); Ju Ren (Tsinghua University, China); Dongxiao Yu (Shandong University, China)
Speaker
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Zhibo Wang, Zhiwei Chang and Jiahui Hu (Zhejiang University, China); Xiaoyi Pang (Wuhan University, China); Jiacheng Du (Zhejiang University, China); Yongle Chen (Taiyuan University of Technology, China); Kui Ren (Zhejiang University, China)
Speaker Zhiwei Chang (Zhejiang University)
Hi, I am Zhiwei Chang, a graduate student at the School of Computer Science, Zhejiang University and my research focuses on security and privacy issues in federated learning.
Session Chair
Qin Hu (IUPUI, USA)
E-1: Network Measurement
Robust or Risky: Measurement and Analysis of Domain Resolution Dependency
Shuhan Zhang (Tsinghua University, China); Shuai Wang (Zhongguancun Laboratory, China); Dan Li (Tsinghua University, China)
Speaker Shuhan Zhang (Tsinghua University)
Accelerating Sketch-based End-Host Traffic Measurement with Automatic DPU Offloading
Xiang Chen, Xi Sun, Wenbin Zhang, Zizheng Wang, Xin Yao, Hongyan Liu and Gaoning Pan (Zhejiang University, China); Qun Huang (Peking University, China); Xuan Liu (Yangzhou University & Southeast University, China); Haifeng Zhou and Chunming Wu (Zhejiang University, China)
Speaker
Effective Network-Wide Traffic Measurement: A Lightweight Distributed Sketch Deployment
Fuliang Li and Kejun Guo (Northeastern University, China); Jiaxing Shen (Lingnan University, Hong Kong); Xingwei Wang (Northeastern University, China)
Speaker Kejun Guo(Northeastern University, China)
QM-RGNN: An Efficient Online QoS Measurement Framework with Sparse Matrix Imputation for Distributed Edge Clouds
Heng Zhang, Zixuan Cui, Shaoyuan Huang, Deke Guo and Xiaofei Wang (Tianjin University, China); Wenyu Wang (Shanghai Zhuichu Networking Technologies Co., Ltd., China)
Speaker
Session Chair
Deepak Nadig (Purdue University, USA)
F-1: Network Security 1
A De-anonymization Attack Against Downloaders in Freenet
Yonghuan Xu, Ming Yang and Zhen Ling (Southeast University, China); Zixia Liu (Anhui University of Technology, China); Xiaodan Gu (Southeast University, China); Lan Luo (Anhui University of Technology, China)
Speaker
Trace-agnostic and Adversarial Training-resilient Website Fingerprinting Defense
Litao Qiao, Bang Wu, Heng Li, Cuiying Gao and Wei Yuan (Huazhong University of Science and Technology, China); Xiapu Luo (The Hong Kong Polytechnic University, Hong Kong)
Speaker
Explanation-Guided Backdoor Attacks on Model-Agnostic RF Fingerprinting
Tianya Zhao and Xuyu Wang (Florida International University, USA); Junqing Zhang (University of Liverpool, United Kingdom (Great Britain)); Shiwen Mao (Auburn University, USA)
Speaker Tianya Zhao (Florida International University)
Tianya Zhao is a second-year Ph.D. student studying computer science at FIU, supervised by Dr. Xuyu Wang. Prior to this, he received his Master's degree from Carnegie Mellon University and Bachelor's degree from Hunan University. In his current Ph.D. program, he is focusing on AIoT, AI Security, Wireless Sensing, and Smart Health.
Exploiting Miscoordination of Microservices in Tandem for Effective DDoS Attacks
Anat Bremler-Barr (Tel-Aviv University, Israel); Michael Czeizler (Reichman University, Israel); Hanoch Levy (Tel Aviv University, Israel); Jhonatan Tavori (Tel-Aviv University, Israel)
Speaker Jhonatan Tavori (TAU)
Jhonatan is a fourth-year Computer Science PhD Student at TAU, advised by Prof. Hanoch Levy. His research focuses on analysing the operation of stochastic systems and networks performance in the presence of malicious behavior.
Session Chair
Hrishikesh B Acharya (Rochester Institute of Technology, USA)
Conference Lunch (for Registered Attendees)
A-2: Blockchains
A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN
Zhuo Chen, Liehuang Zhu and Peng Jiang (Beijing Institute of Technology, China); Jialing He (Chongqing University, China); Zijian Zhang (Beijing Institute of Technology, China)
Speaker Zhuo Chen (Beijing Institute of Technology)
Zhuo Chen received the B.E. degree in information security from the North China Electric Power University, Beijing, China, in 2019. He is currently pursuing the Ph.D. degree with the School of Cyberspace Science and Technology, Beijing Institute of Technology. His current research interests include blockchain technology and covert communication.
Broker2Earn: Towards Maximizing Broker Revenue and System Liquidity for Sharded Blockchains
Qinde Chen, Huawei Huang and Zhaokang Yin (Sun Yat-Sen University, China); Guang Ye (Sen Yat-Sen University, China); Qinglin Yang (Sun Yat-Sen University, China)
Speaker
FileDES: A Secure Scalable and Succinct Blockchain-based Decentralized Encrypted Storage Network
Minghui Xu (Shandong University, China); JiaHao Zhang (ShanDong University, China); Hechuan Guo, Xiuzhen Cheng and Dongxiao Yu (Shandong University, China); Qin Hu (IUPUI, USA); Yijun Li and Yipu Wu (BaishanCloud, China)
Speaker
Account Migration across Blockchain Shards using Fine-tuned Lock Mechanism
Huawei Huang, Yue Lin and Zibin Zheng (Sun Yat-Sen University, China)
Speaker
Session Chair
Xiaodong Lin (University of Guelph, Canada)
B-2: MIMO and Beamforming
NOMA-Enhanced Quantized Uplink Multi-user MIMO Communications
Thanh Phung Truong, Anh-Tien Tran and Van Dat Tuong (Chung-Ang University, Korea (South)); Nhu-Ngoc Dao (Sejong University, Korea (South)); Sungrae Cho (Chung-Ang University, Korea (South))
Speaker
A Learning-only Method for Multi-Cell Multi-User MIMO Sum Rate Maximization
Qingyu Song (The Chinese University of Hong Kong, Hong Kong); Juncheng Wang (Hong Kong Baptist University, Hong Kong); Jingzong Li (City University of Hong Kong, Hong Kong); Guochen Liu (Huawei Noah's Ark Lab, China); Hong Xu (The Chinese University of Hong Kong, Hong Kong)
Speaker Qingyu Song (The Chinese University of Hong Kong)
Qingyu is a Ph.D. student at The Chinese University of Hong Kong. He got an M.S. and B.S. at Tsinghua University and Harbin Institute of Technology in 2021 and 2018, respectively. He focuses on utilizing machine learning techniques to solve optimization problems. His work has been accepted by CVPR, INFOCOM, ITSC, etc.
HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers
Debamita Ghosh and Manjesh K Hanawal (Indian Institute of Technology Bombay, India); Nikola Zlatanov (Innopolis University, Russia)
Speaker
FTP: Enabling Fast Beam-Training for Optimal mmWave Beamforming
Wei-Han Chen, Xin Liu, Kannan Srinivasan and Srinivasan Parthasarathy (The Ohio State University, USA)
Speaker
Session Chair
Joerg Widmer (IMDEA Networks Institute, Spain)
C-2: Wireless Security
Silent Thief: Password Eavesdropping Leveraging Wi-Fi Beamforming Feedback from POS Terminal
Siyu Chen, Hongbo Jiang, Jingyang Hu, Zhu Xiao and Daibo Liu (Hunan University, China)
Speaker Siyu Chen (Hunan University)
Siyu Chen received the B.S. degree in communication engineering from Hunan University, Changsha, China, in 2021, where he is currently pursuing the Ph.D. degree with the College of Computer Science and Electronic Engineer, Hunan University. He has published papers in IEEE INFOCOM, IEEE IoTJ, IEEE TMC and IEEE JSAC. His research interests lie in the area of wireless sensing and Internet of Things security.
Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion
Jiahui Li and Geng Sun (Jilin University, China); Qingqing Wu (Shanghai Jiao Tong University, China); Shuang Liang (Northeast Normal University, China); Pengfei Wang (Dalian University of Technology, China); Dusit Niyato (Nanyang Technological University, Singapore)
Speaker
EchoLight: Sound Eavesdropping based on Ambient Light Reflection
Guoming Zhang, Zhijie Xiang, Heqiang Fu, Yanni Yang and Pengfei Hu (Shandong University, China)
Speaker Heqiang Fu (Shandong University)
Heqiang Fu is currently working towards a master’s degree at the School of Computer Science, Shandong University, China. His recent research has centered around Internet of Things (IoT) security.
mmEar: Push the Limit of COTS mmWave Eavesdropping on Headphones
Xiangyu Xu, Yu Chen and Zhen Ling (Southeast University, China); Li Lu (Zhejiang University, China); Luo Junzhou (Southeast University, China); Xinwen Fu (University of Massachusetts Lowell, USA)
Speaker Yu Chen(Southeast University)
Session Chair
Edmundo Monteiro (University of Coimbra, Portugal)
D-2: Multi-Armed Bandits
Achieving Regular and Fair Learning in Combinatorial Multi-Armed Bandit
Xiaoyi Wu and Bin Li (The Pennsylvania State University, USA)
Speaker
Adversarial Combinatorial Bandits with Switching Cost and Arm Selection Constraints
Yin Huang (University of Miami, USA); Qingsong Liu (Tsinghua University, China); Jie Xu (University of Miami, USA)
extensions to the basic MAB framework. In this paper, we focus on an adversarial MAB problem inspired by real-world systems with combinatorial semi-bandit arms, switching costs, and anytime cumulative arm selection constraints. To tackle this challenging problem, we introduce the Block-structured Follow-the-Regularized-Leader (B-FTRL) algorithm. Our approach employs a hybrid Tsallis-Shannon entropy regularizer in arm selection and incorporates a block structure that divides time into blocks to minimize arm switching costs. The theoretical analysis shows that B-FTRL achieves a reward regret bound of \(O(T^\frac{2a-b+1}{1+a} + T^\frac{b}{1+a})\) and a switching regret bound of \(O(T^\frac{1}{1+a})\). By carefully selecting the values of \(a\) and \(b\), we are able to limit the total regret to \(O(T^{2/3})\) while satisfying the arm selection constraints in expectation. This outperforms the state-of-the-art regret bound of \(O(T^{3/4})\) and expected constraint violation bound \(o(1)\), which are derived in less challenging stochastic reward environments. Additionally, we provide a high-probability constraint violation bound of \(O(\sqrt{T})\). Numerical results are presented to demonstrate its superiority in comparison to other existing methods.
Speaker
Backlogged Bandits: Cost-Effective Learning for Utility Maximization in Queueing Networks
Juaren Steiger (Queen's University, Canada); Bin Li (The Pennsylvania State University, USA); Ning Lu (Queen's University, Canada)
Speaker Juaren Steiger (Queen's University)
Juaren Steiger is a PhD student at Queen's University in Canada studying machine learning and its applications to communication networks.
Edge-MSL: Split Learning on the Mobile Edge via Multi-Armed Bandits
Taejin Kim (CACI Intl. Inc. & Carnegie Mellon University, USA); Jinhang Zuo (University of Massachusetts Amherst & California Institute of Technology, USA); Xiaoxi Zhang (Sun Yat-sen University, China); Carlee Joe-Wong (Carnegie Mellon University, USA)
Speaker Taejin Kim
Taejin Kim is currently a research engineer at CACI, currently working in the area of distributed machine learning systems and security. Prior to joining CACI, he was a PhD student at Carnegie Mellon University, performing research in the area of mobile edge computing and distributed learning optimization.
Session Chair
Bo Ji (Virginia Tech, USA)
E-2: Scheduling 1
Age-minimal CPU Scheduling
Mengqiu Zhou and Meng Zhang (Zhejiang University, China); Howard Yang (Zhejiang University, China & University of Illinois at Urbana Champaign (UIUC), USA); Roy Yates (Rutgers University, USA)
Speaker
Cur-CoEdge: Curiosity-Driven Collaborative Request Scheduling in Edge-Cloud Systems
Yunfeng Zhao and Chao Qiu (Tianjin University, China); Xiaoyun Shi (TianJin University, China); Xiaofei Wang (Tianjin Key Laboratory of Advanced Networking, Tianjin University, China); Dusit Niyato (Nanyang Technological University, Singapore); Victor C.M. Leung (Shenzhen University, China & The University of British Columbia, Canada)
Speaker Yunfeng Zhao (Tianjin University)
Yunfeng Zhao is a PhD candidate at the College of Intelligence and Computing, Tianjin University, China. Her current research interests include edge computing, edge intelligence, and distributed machine learning.
InNetScheduler: In-network scheduling for time- and event-triggered critical traffic in TSN
Xiangwen Zhuge, Xinjun Cai, Xiaowu He, Zeyu Wang, Fan Dang, Wang Xu and Zheng Yang (Tsinghua University, China)
Speaker Xiangwen Zhuge (Tsinghua Univeristy)
Xiangwen Zhuge is currently a PhD student in Software Engineering at Tsinghua University, where he also completed my undergraduate studies. His research primarily focuses on time-sensitive networking(TSN).
Learning-based Scheduling for Information Gathering with QoS Constraints
Qingsong Liu, Weihang Xu and Zhixuan Fang (Tsinghua University, China)
Speaker
Session Chair
Mohamed Hefeeda (Simon Fraser University, Canada)
F-2: Network Security 2
WFGuard: An Effective Fuzz-Testing-Based Traffic Morphing Defense Against Website Fingerprinting
Zhen Ling and Gui Xiao (Southeast University, China); Lan Luo (Anhui University of Technology, China); Rong Wang and Xiangyu Xu (Southeast University, China); Guangchi Liu (Southeast University, USA)
Speaker Gui Xiao (Southeast University)
Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs
Quang Huy Ngo (The University of Adelaide, Australia); Mingyu Guo and Hung Xuan Nguyen (University of Adelaide, Australia)
Speaker
PTPsec: Securing the Precision Time Protocol Against Time Delay Attacks Using Cyclic Path Asymmetry Analysis
Andreas Finkenzeller and Oliver Butowski (Technical University of Munich, Germany); Emanuel Regnath (Siemens AG, Germany); Mohammad Hamad and Sebastian Steinhorst (Technical University of Munich, Germany)
Speaker
CARBINE: Exploring Additional Properties of HyperLogLog for Secure and Robust Flow Cardinality Estimation
Damu Ding (University of Oxford, United Kingdom (Great Britain))
Speaker
Session Chair
Jun Zhao (Nanyang Technological University, Singapore)
Coffee Break
A-3: Video Streaming
Gecko: Resource-Efficient and Accurate Queries in Real-Time Video Streams at the Edge
Liang Wang (Huazhong University of Science and Technology, China); Xiaoyang Qu (Ping An Technology (Shenzhen) Co., Ltd, China); Jianzong Wang (Pingan, China); Guokuan Li and Jiguang Wan (Huazhong University of Science and Technology, China); Nan Zhang (Ping An Technology (Shenzhen) Co., Ltd., China); Song Guo (The Hong Kong University of Science and Technology, Hong Kong); Jing Xiao (Ping An Insurance Company of China,Ltd., China)
Speaker Liang Wang (Huazhong University of Science and Technology)
Liang Wang is a third-year Master's student in the PDSL group at Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, advised by Prof. Jiguang Wan. His current research interests focus on computing and storage systems in cloud and edge environments. Before joining HUST, he earned a Bachelor's degree in Software Engineering from Wuhan University in 2021. Liang has also completed internships at PingCAP, Huawei Cloud, and Ping An Technology.
Rosevin: Employing Resource- and Rate-Adaptive Edge Super-Resolution for Video Streaming
Xiaoxi Zhang (Sun Yat-sen University, China); Haoran Xu (Sun Yat-Sen University, China); Longhao Zou (Peng Cheng Laboratory, Shenzhen & Southern University of Science and Technology, China); Jingpu Duan (Peng Cheng Laboratory, China); Chuan Wu (The University of Hong Kong, Hong Kong); Yali Xue and ZuoZhou Chen (Peng Cheng Laboratory, China); Xu Chen (Sun Yat-sen University, China)
Speaker
TBSR: Tile-Based 360° Video Streaming with Super-Resolution on Commodity Mobile Devices
Lei Zhang and Haobin Zhou (Shenzhen University, China); Haiyang Wang (University of Minnesota at Duluth, USA); Laizhong Cui (Shenzhen University, China)
We present the designs of three key mechanisms, including a rate adaptation method with macro tile grouping to reduce decoding computations, a decoding and SR scheduler for different types of tasks to achieve the best cost efficiency, and the workload adjustment method to control the amount of tasks given the available capabilities. We further implement the TBSR prototype. Our performance evaluation shows that TBSR outperforms the existing methods, improving QoE quality by up to 32\% and bandwidth savings by 26\%.
Speaker
Smart Data-Driven Proactive Push to Edge Network for User-Generated Videos
Xiaoteng Ma (Tsinghua University, China); Qing Li (Peng Cheng Laboratory, China); Junkun Peng (Tsinghua University, China); Gareth Tyson (The Hong Kong University of Science and Technology & Queen Mary University of London, Hong Kong); Ziwen Ye and Shisong Tang (Tsinghua University, China); Qian Ma (ByteDance Technology Co., Ltd., China); Shengbin Meng (ByteDance Inc., China); Gabriel-Miro Muntean (Dublin City University, Ireland)
Speaker Xiaoteng Ma
Xiaoteng Ma received his B.Eng. degree in 2017 and his Ph.D. in 2024. His research interests include edge-assisted multimedia delivery and resource allocation in hybrid cloud-edge-client networks.
Session Chair
Lin Wang (Paderborn University, Germany)
B-3: Satellite networks
Your Mega-Constellations Can be Slim: A Cost-Effective Approach for Constructing Survivable and Performant LEO Satellite Networks
Zeqi Lai, Yibo Wang, Hewu Li and Qian Wu (Tsinghua University, China); Qi Zhang (Zhongguancun Laboratory, China); Yunan Hou (Beijing Forestry University, China); Jun Liu and Yuanjie Li (Tsinghua University, China)
In this paper, we investigate the problem: from a network perspective, how many satellites exactly do we need to construct a survivable and performant LSN? To answer this question, we first formulate the survivable and performant LSN design (SPLD) problem, which aims to find the minimum number of needed satellites to construct an LSN that can provide a sufficient amount of redundant paths, link capacity, and acceptable latency for all communication pairs served by the LSN. Second, to efficiently solve the SPLD problem, we propose MEGAREDUCE, a requirement-driven optimization mechanism, which can calculate feasible solutions for SPLD in polynomial time. Finally, we conduct extensive trace-driven simulations to verify MEGAREDUCE's cost-effectiveness in constructing survivable and performant LSNs on demand and showcase how MEGAREDUCE can help optimize the incremental deployment and long-term maintenance of future LSNs.
Speaker Zeqi Lai (Tsinghua University)
Accelerating Handover in Mobile Satellite Network
Jiasheng Wu, Shaojie Su, Xiong Wang, Jingjing Zhang and Yue Gao (Fudan University, China)
Speaker
SKYCASTLE: Taming LEO Mobility to Facilitate Seamless and Low-latency Satellite Internet Services
Jihao Li, Hewu Li, Zeqi Lai, Qian Wu and Weisen Liu (Tsinghua University, China); Xiaomo Wang (China Academy of Electronics and Information Technology, China); Yuanjie Li and Jun Liu (Tsinghua University, China); Qi Zhang (Zhongguancun Laboratory, China)
To facilitate seamless and low-latency Internet services, this paper presents SKYCASTLE, a novel network-based global mobility management mechanism. SKYCASTLE incorporates two key techniques to address connection interruptions caused by space-ground handovers. First, to reduce connection interruptions, SKYCASTLE adopts distributed satellite anchors to track the location changes of mobile nodes, manage handovers and accelerate routing convergence. Second, SKYCASTLE leverages an anchor manager to schedule MM functionalities at satellites to reduce deployment costs while guaranteeing latency. Extensive evaluations combining real constellation information and popular flight trajectories demonstrate that: SKYCASTLE can improve uninterrupted time by up to 55.8% and reduce latency by 47.8%.
Speaker Jihao Li (Tsinghua University)
Jihao Li is pursuing his Ph.D. degree in the Department of Computer Science and Technology, Tsinghua University. His current research areas include the routing, transport and mobility management of integrated space and terrestrial networks.
Resource-efficient In-orbit Detection of Earth Objects
QiYang Zhang (Beijing University of Posts & Telecommunications, China); Xin Yuan and Ruolin Xing (Beijing University of Posts and Telecommunications, China); Yiran Zhang (Beijing University of Posts and Telecommunication, China); Zimu Zheng (Huawei Technologies Co., Ltd, China); Xiao Ma and Mengwei Xu (Beijing University of Posts and Telecommunications, China); Schahram Dustdar (Vienna University of Technology, Austria); Shangguang Wang (Beijing University of Posts and Telecommunications, China)
Speaker Qiyang Zhang (Beijing University of Posts and Telecommunications)
Qiyang Zhang is a Ph.D. candidate in computer science at the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications. He is also a visiting student in the Distributed Systems Group at TU Wien from December 2022 to December 2023. His research interests include Satellite Edge Computing and Edge Intelligence.
Session Chair
Dimitrios Koutsonikolas (Northeastern University, USA)
C-3: Intrusion-Detection Systems
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction
Ruoyu Li (Tsinghua University, China); Qing Li (Peng Cheng Laboratory, China); Yu Zhang (Tsinghua University & Shanghai Artificial Intelligence Laboratory, China); Dan Zhao (Peng Cheng Laboratory, China); Xi Xiao and Yong Jiang (Graduate School at Shenzhen, Tsinghua University, China)
Speaker Ruoyu Li (Tsinghua University)
Ruoyu Li is a Ph.D. candidate at Tsinghua University, majoring in computer science and technology. Before that, he received a B.S. degree in information security from Huazhong University of Science and Technology, Wuhan, China, in 2017, and an M.S. degree in computer science from Columbia University, New York, USA, in 2019. He is also a research intern with Peng Cheng Laboratory, Shenzhen, China. His research interests mainly include intrusion/anomaly detection systems, Internet of Things security, programmable networking, and explainable/trustworthy AI.
SPIDER: A Semi-Supervised Continual Learning-based Network Intrusion Detection System
Suresh Kumar Amalapuram and Sumohana Channappayya (Indian Institute of Technology Hyderabad, India); Bheemarjuna Reddy Tamma (IIT Hyderabad, India)
Speaker
AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection
Xinchen Zhang and Running Zhao (The University of Hong Kong, Hong Kong); Zhihan Jiang (The University of Hong Kong, China); Zhicong Sun (The Hong Kong Polytechnic University, Hong Kong); Yulong Ding (Southern University of Science and Technology, China); Edith C.-H. Ngai (The University of Hong Kong & Uppsala University, Hong Kong); Shuang-Hua Yang (Southern University of Science and Technology, China)
Speaker Ke Wang
RIDS: Towards Advanced IDS via RNN Model and Programmable Switches Co-Designed Approaches
Ziming Zhao (Zhejiang University, China); Zhaoxuan Li (Institute of Information Engineering Chinese Academy of Sciences, China); Zhuoxue Song and Fan Zhang (Zhejiang University, China); Binbin Chen (Singapore University of Technology and Design, Singapore)
Speaker
Session Chair
Tamer Nadeem (Virginia Commonwealth University, USA)
D-3: Federated Learning 2
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
Xiaoxin Su (Shenzhen University, China); Yipeng Zhou (Macquarie University, Australia); Laizhong Cui (Shenzhen University, China); John C.S. Lui (The Chinese University of Hong Kong, Hong Kong); Jiangchuan Liu (Simon Fraser University, Canada)
Speaker
Titanic: Towards Production Federated Learning with Large Language Models
Ningxin Su, Chenghao Hu and Baochun Li (University of Toronto, Canada); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker Ningxin Su (University of Toronto)
Ningxin Su is a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering, University of Toronto, under the supervision of Prof. Baochun Li. She received her M.E. and B.E. degrees from the University of Sheffield and Beijing University of Posts and Telecommunications in 2020 and 2019, respectively. Her research area includes distributed machine learning, federated learning and networking. Her website is located at ningxinsu.github.io.
FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value
Yiqi Liu, Shan Chang and Ye Liu (Donghua University, China); Bo Li (Hong Kong University of Science and Technology, Hong Kong); Cong Wang (City University of Hong Kong, Hong Kong)
Speaker Yiqi Liu (Donghua University)
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Pengchao Han (Guangdong University of Technology, China); Shiqiang Wang (IBM T. J. Watson Research Center, USA); Yang Jiao (Tongji University, China); Jianwei Huang (The Chinese University of Hong Kong, Shenzhen, China)
Speaker
Session Chair
Christopher G. Brinton (Purdue University, USA)
E-3: Scheduling 2
Monitoring Correlated Sources: AoI-based Scheduling is Nearly Optimal
Rudrapatna Vallabh Ramakanth, Vishrant Tripathi and Eytan Modiano (MIT, USA)
Speaker
Scheduling Stochastic Traffic With End-to-End Deadlines in Multi-hop Wireless Networks
Christos Tsanikidis and Javad Ghaderi (Columbia University, USA)
Speaker
Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES
Marcel Blöcher (SAP SE & TU Darmstadt, Germany); Nils Nedderhut (Vivenu & TU Darmstadt, Germany); Pavel Chuprikov (Università della Svizzera Italiana, Switzerland); Ramin Khalili (Huawei Technologies, Germany); Patrick Eugster (Università Della Svizzera Italiana (USI), Switzerland); Lin Wang (Paderborn University, Germany)
We fill this gap by presenting FUMES, a reinforcement learning based distributed agent design for the runtime scheduling problem of assigning packets undergoing treatment by network function chains to network function instances. Our system design consists of multiple distributed agents that cooperatively work on the scheduling problem. A key design choice enables agents, once trained, to be applicable for unknown chains and traffic patterns including branching, and different environments inlcuding link failures. The paper presents the system design and shows its suitability for realistic deployments. We empirically compare FUMES with state-of-the-art runtime scheduling solutions showing improved scheduling decisions at lower server capacity.
Speaker Marcel Blöcher (SAP & TU Darmstadt)
Marcel Blöcher is currently an architect at SAP working on resource scheduling of SAP’s own data centers. He received his Ph.D. from TU Darmstadt (Germany) in 2021. His research interests is on a broad range of resources scheduling problems.
EdgeTimer: Adaptive Multi-Timescale Scheduling in Mobile Edge Computing with Deep Reinforcement Learning
Yijun Hao, Shusen Yang, Fang Li, Yifan Zhang, Shibo Wang and Xuebin Ren (Xi'an Jiaotong University, China)
We notice that the adaptive timescales would significantly improve the trade-off between the operation cost and delay performance. Based on this insight, we propose EdgeTimer, the first work to automatically generate adaptive timescales to update multi-layer scheduling decisions using deep reinforcement learning (DRL). First, EdgeTimer uses a three-layer hierarchical DRL framework to decouple the multi-layer decision-making task into a hierarchy of independent sub-tasks for improving learning efficiency. Second, to cope with each sub-task, EdgeTimer adopts a safe multi-agent DRL algorithm for decentralized scheduling while ensuring system reliability. We apply EdgeTimer to a wide range of Kubernetes scheduling rules, and evaluate it using production traces with different workload patterns. Extensive trace-driven experiments demonstrate that EdgeTimer can learn adaptive timescales, irrespective of workload patterns and built-in scheduling rules. It obtains up to 9.1x more profit than existing approaches without sacrificing the delay performance.
Speaker
Session Chair
Alex Sprintson (Texas A&M University, USA)
F-3: Network Security 3
Periscoping: Private Key Distribution for Large-Scale Mixnets
Shuhao Liu (Shenzhen Institute of Computing Sciences, China); Li Chen (University of Louisiana at Lafayette, USA); Yuanzhong Fu (Unaffiliated, China)
This paper presents Periscoping, a key distribution protocol for mixnets at scale. Periscoping relaxes the download-all requirement for clients. Instead, it allows a client to selectively download a constant number of entries of the key directory, while guaranteeing the privacy of selections. Periscoping achieves this goal via a novel Private Information Retrieval scheme, constructed based on constrained Pseudorandom Functions. Moreover, the protocol is integrated seamlessly into the mixnet operations, readily applicable to existing mixnet systems as an extension at a minimal cost. Our experiments show that, with millions of mixes, it can reduce the traffic load of a mixnet by orders of magnitude, at a minor computational and bandwidth overhead.
Speaker Shuhao Liu (Shenzhen Institute of Computing Sciences)
Detecting Adversarial Spectrum Attacks via Distance to Decision Boundary Statistics
Wenwei Zhao and Xiaowen Li (University of South Florida, USA); Shangqing Zhao (University of Oklahoma, USA); Jie Xu (University of Miami, USA); Yao Liu and Zhuo Lu (University of South Florida, USA)
Speaker
RF-Parrot: Wireless Eavesdropping on Wired Audio
Yanni Yang and Genglin Wang (Shandong University, China); Zhenlin An (Princeton University, USA); Pengfei Hu, Xiuzhen Cheng and Guoming Zhang (Shandong University, China)
Speaker
BlueKey: Exploiting Bluetooth Low Energy for Enhanced Physical-Layer Key Generation
Yawen Zheng and Fan Dang (Tsinghua University, China); Zihao Yang (Yanshan University, China); Jinyan Jiang and Wang Xu (Tsinghua University, China); Lin Wang (Yanshan University, China); Kebin Liu and Xinlei Chen (Tsinghua University, China); Yunhao Liu (Tsinghua University & The Hong Kong University of Science and Technology, China)
Speaker Yawen Zheng (Tsinghua University)
Session Chair
Pradeeban Kathiravelu (University of Alaska Anchorage, USA)
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