Advanced Topics in AI for Computing and Networking (Winter 2024/2025)

From NET Wiki
Jump to navigation Jump to search

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]

21. Two-Stream Network for Sign Language Recognition and Translation [21]

22. LiveVV: Human-Centered Live Volumetric Video Streaming System [22]

23. MetaStream: Live Volumetric Content Capture, Creation, Delivery, and Rendering in Real Time [23]

Schedule

W1: Open Talk (24.10)

W2: Select papers and create schedule

W4: [18] AutoDroid: LLM-powered Task Automation in Android

W6: [21] Two-Stream Network for Sign Language Recognition and Translation

W8: [7] RDMA over Ethernet for Distributed Training at Meta Scale

W10:[3] CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

W12:[22] LiveVV: Human-Centered Live Volumetric Video Streaming System

W14:[19] m3: Accurate Flow-Level Performance Estimation using Machine Learning


!! 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)


Retrieved from "https://wiki.net.informatik.uni-goettingen.de/index.php?title=Advanced_Topics_in_AI_for_Computing_and_Networking_(Winter_2024/2025)&oldid=8594"