Advanced Topics in AI for Computing and Networking (Summer 2024)

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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. FedAdapter: Efficient Federated Learning for Modern NLP [1]

2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [2]

3. mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications [3]

4. NeRF2: Neural Radio-Frequency Radiance Fields [4]

5. Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot Learning [5]

6. Practically Adopting Human Activity Recognition [6]

7. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [7]

8. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [8]

9. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [9]

10. Batch Adaptative Streaming for Video Analytics [10]

11. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [11]

12. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [12]

13. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [13]

14. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [14]

15. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [15]

16. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [16]

17. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [17]

18. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [18]

19. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [19]

20. Top-K Deep Video Analytics: A Probabilistic Approach [20]

Schedule

W1: Open Talk (11.04)

W2: Select papers and create schedule

W4: Paper ID:

W6: ...

W8: ...

W10: ...

W12: ...

W14: ..

!! xx.xx deadline for registration on Flexnow

Final Presentation (xx.07)

  • 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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