Advanced Topics in AI for Computing and Networking (Winter 2024/2025): Difference between revisions

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==List of Papers ==
==List of Papers ==
1. ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction [https://dl.acm.org/doi/10.1145/3603269.3604819]
1. NetLLM: Adapting Large Language Models for Networking [https://doi.org/10.1145/3651890.3672268]


2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [https://dl.acm.org/doi/10.1145/3570361.3592517]
2. m3: Accurate Flow-Level Performance Estimation using Machine Learning [https://doi.org/10.1145/3651890.3672243]


3. mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications [https://dl.acm.org/doi/10.1145/3570361.3592515]
3. CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving [https://doi.org/10.1145/3651890.3672274]


4. NeRF2: Neural Radio-Frequency Radiance Fields [https://dl.acm.org/doi/10.1145/3570361.3592527]
4. Crux: GPU-Efficient Communication Scheduling for Deep Learning Training [https://doi.org/10.1145/3651890.3672239]


5. Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot Learning [https://dl.acm.org/doi/10.1145/3570361.3613288]
5. Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction [https://doi.org/10.1145/3651890.3672250]


6. Practically Adopting Human Activity Recognition [https://dl.acm.org/doi/10.1145/3570361.3613299]
6. Transferable Neural WAN TE for Changing Topologies [https://dl.acm.org/doi/10.1145/3570361.3613299]


7. AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization. [https://ieeexplore.ieee.org/document/10228858]
7. RDMA over Ethernet for Distributed Training at Meta Scale [https://doi.org/10.1145/3651890.3672233]


8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [https://ieeexplore.ieee.org/document/10228863]  
8. RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic Engineering [https://doi.org/10.1145/3651890.3672231]  


9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [https://ieeexplore.ieee.org/document/10228869]
9. TopFull: An Adaptive Top-Down Overload Control for SLO-Oriented Microservices [https://doi.org/10.1145/3651890.3672253]


10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [https://ieeexplore.ieee.org/document/10228926]
10. Smart Data-Driven Proactive Push to Edge Network [https://doi.org/10.1109/INFOCOM52122.2024.10621410]


11. Two-level Graph Caching for Expediting Distributed GNN Training [https://ieeexplore.ieee.org/document/10228911]
11. A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN [https://doi.org/10.1109/INFOCOM52122.2024.10621377]


12. From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning [https://ieeexplore.ieee.org/document/10228909]
12. Det-RAN: Data-Driven Cross-Layer Real-Time Attack Detection in 5G Open RANs [https://doi.org/10.1109/INFOCOM52122.2024.10621223]


13. HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN [https://ieeexplore.ieee.org/document/10229047]
13. TITANIC: Towards Production Federated Learning with Large Language Models [https://doi.org/10.1109/INFOCOM52122.2024.10621164]


14. AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics [https://ieeexplore.ieee.org/document/10228933]
14. Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization [https://doi.org/10.1109/INFOCOM52122.2024.10621369]


15. DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks [https://ieeexplore.ieee.org/document/10228962]
15. Predicting Multi-Scale Information Diffusion via Minimal Substitution Neural Networks [https://doi.org/10.1109/INFOCOM52122.2024.10621418]


16. Federated Few-Shot Learning for Mobile NLP [https://dl.acm.org/doi/10.1145/3570361.3613277]
16. Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation [https://doi.org/10.1145/3636534.3649367]


17. A Joint Analysis of Input Resolution and Quantization Precision in Deep Learning [https://dl.acm.org/doi/10.1145/3570361.3615753]
17. Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving [https://doi.org/10.1145/3636534.3649352]


18. Automated Spray Control using Deep Learning and Image Processing [https://dl.acm.org/doi/10.1145/3570361.3615757]
18. AutoDroid: LLM-powered Task Automation in Android [https://doi.org/10.1145/3636534.3649379]


19. Cross-Modal Perception for Customer Service [https://dl.acm.org/doi/10.1145/3570361.3615751]
19. FlexNN: Efficient and Adaptive DNN Inference on Memory-Constrained Edge Devices [https://doi.org/10.1145/3636534.3649391]


20. Cross-modal meta-learning for WiFi-based human activity recognition [https://dl.acm.org/doi/10.1145/3570361.3615754]
20. Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices [https://doi.org/10.1145/3636534.3649363]


==Schedule==
==Schedule==

Latest revision as of 13:18, 14 October 2024

Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1123
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan; Wenfang Wu;
Teaching assistant: [NA]
Time: Thursday 14:00-16:00


Announcements

Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions.

Course Overview

The purpose of this seminar is to discuss some advanced topics in computer networks. This course is a theory-oriented research seminar (5 ECTS, 2 SWS), held on a weekly base and comprises the following components:

  • Weekly Presentation + Weekly Paper Reading and Discussion 40%
  • Final Presentation 40%
  • Final Report 20%

The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.

Requirements

  • Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
    • Summary of the paper
    • Pros and cons of the paper (your conclusion)
    • NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 on Wedesday). => the review form is available at [Paper_Review_Form_ATCN_WS201112.doc]
  • During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
  • In the middle of the semester, everyone is requested to prepare:
    • Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
    • Final presentation: each presentation lasts for ~20 minutes, plus ~10 minutes Q&A

List of Papers

1. NetLLM: Adapting Large Language Models for Networking [1]

2. m3: Accurate Flow-Level Performance Estimation using Machine Learning [2]

3. CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving [3]

4. Crux: GPU-Efficient Communication Scheduling for Deep Learning Training [4]

5. Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction [5]

6. Transferable Neural WAN TE for Changing Topologies [6]

7. RDMA over Ethernet for Distributed Training at Meta Scale [7]

8. RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic Engineering [8]

9. TopFull: An Adaptive Top-Down Overload Control for SLO-Oriented Microservices [9]

10. Smart Data-Driven Proactive Push to Edge Network [10]

11. A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN [11]

12. Det-RAN: Data-Driven Cross-Layer Real-Time Attack Detection in 5G Open RANs [12]

13. TITANIC: Towards Production Federated Learning with Large Language Models [13]

14. Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization [14]

15. Predicting Multi-Scale Information Diffusion via Minimal Substitution Neural Networks [15]

16. Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation [16]

17. Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving [17]

18. AutoDroid: LLM-powered Task Automation in Android [18]

19. FlexNN: Efficient and Adaptive DNN Inference on Memory-Constrained Edge Devices [19]

20. Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices [20]

Schedule

W1: Open Talk (02.11)

W2: Select papers and create schedule

W4: Paper ID:

W6: ...

W8: ...

W10: ...

W12: ...

W14: ..

!! xx.xx deadline for registration on Flexnow

Final Presentation (xx.01)

  • Paper Title:
  • Paper Title:

Report deadline

Final Presentations & Report

  • Final Registration in FlexNow: To Be Announced (TBA).


  • Final Presentation:
    • Each for ~20 minutes, plus ~20 minutes Q&A


  • Final Presentation Slots:
    • To Be Announced (TBA)


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