IEEE INFOCOM 2024
E-8: Machine Learning 2
Deep Learning Models As Moving Targets To Counter Modulation Classification Attacks
Naureen Hoque and Hanif Rahbari (Rochester Institute of Technology, USA)
Speaker
Deep Learning-based Modulation Classification of Practical OFDM signals for Spectrum Sensing
Byungjun Kim (UCSD, USA); Peter Gerstoft (University of California, San Diego, USA); Christoph F Mecklenbräuker (TU Wien, Austria)
Speaker
Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems
Chetna Singhal (Indian Institute of Technology Kharagpur, India); Yashuo Wu (University of California Irvine, USA); Francesco Malandrino (CNR-IEIIT, Italy); Marco Levorato (University of California, Irvine, USA); Carla Fabiana Chiasserini (Politecnico di Torino & CNIT, IEIIT-CNR, Italy)
Speaker
Jewel: Resource-Efficient Joint Packet and Flow Level Inference in Programmable Switches
Aristide Tanyi-Jong Akem (IMDEA Networks Institute, Spain & Universidad Carlos III de Madrid, Spain); Beyza Butun (Universidad Carlos III de Madrid & IMDEA Networks Institute, Spain); Michele Gucciardo and Marco Fiore (IMDEA Networks Institute, Spain)
Speaker
Session Chair
Marilia Curado (University of Coimbra, Portugal)
E-9: Machine Learning 3
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
Xinglin Pan (Hong Kong Baptist University, Hong Kong); Wenxiang Lin and Shaohuai Shi (Harbin Institute of Technology, Shenzhen, China); Xiaowen Chu (The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Hong Kong); Weinong Sun (The Hong Kong University of Science and Technology, Hong Kong); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker
Predicting Multi-Scale Information Diffusion via Minimal Substitution Neural Networks
Ranran Wang (University of Electronic Science and Technology of China, China); Yin Zhang (University of Electronic Science and Technology, China); Wenchao Wan and Xiong Li (University of Electronic Science and Technology of China, China); Min Chen (Huazhong University of Science and Technology, China)
Speaker
Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
Huaiguang Cai (Sun Yat-Sen University, China); Zhi Zhou (Sun Yat-sen University, China); Qianyi Huang (Sun Yat-Sen University, China & Peng Cheng Laboratory, China)
We address this challenge by modeling the relationship between model performance and different retraining and inference configurations first and then propose a linear complexity online algorithm (named \ouralg).
\ouralg solves the original non-convex, integer, time-coupled problem approximately by adjusting the proportion between model retraining and inference according to available real-time computing resources. The competitive ratio of \ouralg is strictly better than the tight competitive ratio of the Inference-Only algorithm (corresponding to the traditional computing paradigm) when data drift occurs for a sufficiently lengthy time, implying the advantages and applications of model inference and retraining co-location paradigm. In particular, \ouralg translates to several heuristic algorithms in different environments. Experiments based on real scenarios confirm the effectiveness of \ouralg.
Speaker
Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization
Tianyu Qi and Yufeng Zhan (Beijing Institute of Technology, China); Peng Li (The University of Aizu, Japan); Yuanqing Xia (Beijing Institute of Technology, China)
Speaker
Session Chair
Marco Fiore (IMDEA Networks Institute, Spain)
E-10: Machine Learning 4
Augment Online Linear Optimization with Arbitrarily Bad Machine-Learned Predictions
Dacheng Wen (The University of Hong Kong, Hong Kong); Yupeng Li (Hong Kong Baptist University, Hong Kong); Francis C.M. Lau (The University of Hong Kong, Hong Kong)
Speaker
Dancing with Shackles, Meet the Challenge of Industrial Adaptive Streaming via Offline Reinforcement Learning
Lianchen Jia (Tsinghua University, China); Chao Zhou (Beijing Kuaishou Technology Co., Ltd, China); Tianchi Huang, Chaoyang Li and Lifeng Sun (Tsinghua University, China)
Speaker
GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy
Junyang Wang, Lan Zhang, Junhao Wang, Mu Yuan and Yihang Cheng (University of Science and Technology of China, China); Qian Xu (BestPay Co.,Ltd,China Telecom, China); Bo Yu (Bestpay Co., Ltd, China Telecom, China)
Speaker
Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive Approach
Xutong Liu (The Chinese University of Hong Kong, Hong Kong); Jinhang Zuo (University of Massachusetts Amherst & California Institute of Technology, USA); Junkai Wang (Fudan University, China); Zhiyong Wang (The Chinese University of Hong Kong, Hong Kong); Yuedong Xu (Fudan University, China); John Chi Shing Lui (Chinese University of Hong Kong, Hong Kong)
Speaker
Session Chair
Walter Willinger (NIKSUN, USA)
E-11: Machine Learning 5
Taming Subnet-Drift in D2D-Enabled Fog Learning: A Hierarchical Gradient Tracking Approach
Evan Chen (Purdue University, USA); Shiqiang Wang (IBM T. J. Watson Research Center, USA); Christopher G. Brinton (Purdue University, USA)
Speaker
Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments
Yajie Zhou (Zhejiang University, China); Xiaoyi Pang (Wuhan University, China); Zhibo Wang and Jiahui Hu (Zhejiang University, China); Peng Sun (Hunan University, China); Kui Ren (Zhejiang University, China)
Speaker
Personalized Prediction of Bounded-Rational Bargaining Behavior in Network Resource Sharing
Haoran Yu and Fan Li (Beijing Institute of Technology, China)
Speaker
PPGSpotter: Personalized Free Weight Training Monitoring Using Wearable PPG Sensor
Xiaochen Liu, Fan Li, Yetong Cao, Shengchun Zhai and Song Yang (Beijing Institute of Technology, China); Yu Wang (Temple University, USA)
Speaker Xiaochen Liu (Beijing Institute of Technology, China)
Session Chair
Yuval Shavitt (Tel-Aviv University, Israel)
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