Advanced Topics in AI for Networking (Winter 2023/2024): Difference between revisions
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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: | 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% | * Weekly Presentation + Weekly Paper Reading and Discussion 40% | ||
* Final Presentation | * Final Presentation 40% | ||
* Final Report | * 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. | 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. | ||
| Line 32: | Line 32: | ||
==List of Papers == | ==List of Papers == | ||
1. | 1. FedAdapter: Efficient Federated Learning for Modern NLP [https://arxiv.org/pdf/2205.10162.pdf] | ||
2. Learning for | 2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [https://dl.acm.org/doi/10.1145/3570361.3592517] | ||
3. | 3. mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications [https://dl.acm.org/doi/10.1145/3570361.3592515] | ||
4. | 4. NeRF2: Neural Radio-Frequency Radiance Fields [https://dl.acm.org/doi/10.1145/3570361.3592527] | ||
5. | 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] | ||
6. | 6. Practically Adopting Human Activity Recognition [https://dl.acm.org/doi/10.1145/3570361.3613299] | ||
7. | 7. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [https://dl.acm.org/doi/pdf/10.1145/3495243.3560519] | ||
8. | 8. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [https://ieeexplore.ieee.org/document/9796960] | ||
9. | 9. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875] | ||
10. | 10. Batch Adaptative Streaming for Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796853] | ||
11. | 11. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796984] | ||
12. | 12. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796732] | ||
13. | 13. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944] | ||
14. | 14. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [https://dl.acm.org/doi/pdf/10.1145/3495243.3558276?casa_token=IlUioU8GgjwAAAAA:ygqCMlQv28WBdO-z65UXYjJIoeVoyEiwVI00D5nw_cNW0N6aHZCEdBzqt5r2A8jGT7pYuU8xJMJGIrs] | ||
15. | 15. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [https://dl.acm.org/doi/pdf/10.1145/3495243.3560528?casa_token=mrLc2kitFkcAAAAA:lKf6MnXcwXxhr0SrODcIX7qP7DrthKc_yp7jZ-2MYoxmnutM4lHPuYXD5DrLrBjS38S15TbVwPSD-NA] | ||
16. | 16. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796926] | ||
17. | 17. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796878] | ||
18. | 18. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796936] | ||
19. | 19. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [https://www.usenix.org/conference/nsdi22/presentation/bhardwaj] | ||
20. | 20. Top-K Deep Video Analytics: A Probabilistic Approach [https://dl.acm.org/doi/pdf/10.1145/3448016.3452786] | ||
==Schedule== | ==Schedule== | ||
W1: Open Talk ( | W1: Open Talk (02.11) | ||
W2: Select papers and create schedule | W2: Select papers and create schedule | ||
| Line 91: | Line 91: | ||
'''!! xx.xx deadline for registration on Flexnow''' | '''!! xx.xx deadline for registration on Flexnow''' | ||
Final Presentation (xx. | Final Presentation (xx.01) | ||
*'''Paper Title''': | *'''Paper Title''': | ||
*'''Paper Title''': | *'''Paper Title''': | ||
| Line 103: | Line 103: | ||
*Final Presentation: | *Final Presentation: | ||
**Each for ~20 minutes, plus ~ | **Each for ~20 minutes, plus ~20 minutes Q&A | ||
| Line 112: | Line 112: | ||
*Final Report: | *Final Report: | ||
**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/) | **Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/) | ||
**Due by 23:59pm 15th | **Due by 23:59pm 15th February. | ||
[[Category:Courses]] | [[Category:Courses]] | ||
Latest revision as of 10:37, 2 November 2023
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 (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)
- Final Report:
- Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
- Due by 23:59pm 15th February.