Real-time, robust, and reliable (R³) machine learning over wireless networks

WINLAB Summer Internship 2025

Group Members: Ayaan Qayyum(GR), Joshua Menezes(GR), Nihal Abdul Muneer(GR), Hasan Ali(UG), Madhav Subramaniyam(UG)

Project Objective

Introduction:

Edge computing is an emerging paradigm for enabling ML/AI applications in mobile networks. This setting presents many challenges in terms of latency, accuracy, security/privacy, and adaptivity to changing environments. The goal of this project will be to implement and evaluate algorithms and approaches which work “on paper” to see how well they work in practice. In particular, students will work on methods for mobile devices to strategically “offload” complex computing tasks to the cloud, approaches for fully decentralized model updating and adaptation that are robust against malicious attacks, and strategies that can enable real-time tracking and control.

Sub-Groups:

Learning to Help (L2H)	Feature Extraction for Distributed Systems (PCA)
The recently proposed Learning to Help (L2H) model proposed training a server model given a fixed local (client) model. This differs from the Learning to Defer (L2D) framework which trains the client for a fixed (expert) server. L2H demonstrates its applicability in a number of different scenarios of practical interest in which access to the server may be limited by cost, availability, or policy.	Implement a distributed feature extraction method, specifically Principal Component Analysis (PCA), on the Orbit testbed. Enable multiple nodes in the Orbit network to collaboratively learn the eigenvectors to reduce the dimensions of new data samples. This compressed data will then be fed into a pre-trained machine learning model for inference. The central idea is that collaboration among nodes can speed up the process of learning these eigenvectors improving the efficiency of our learning or inference tasks.

Progress

Week 1

​Week 1 Slides

• Read the L2H paper
• Read the PCA paper

Week 2

​Week 2 Slides

• Finished all given papers
• Reviewed ML Concepts
• Practiced Cosmos (Linux, Vim, etc.)
• Set up IDE
• Set up project Gitlab

Week 3

​Week 3 Slides

• Officially split into sub-groups
  - L2H (Joshua & Madhav)
  - PCA (Aayan, Nihal, & Hasan)

L2H	PCA
- Designed Model Architecture based on the paper - Trained all the models - Ran L2H system on one node - Ran multiclass L2H across nodes	- Created an example distributed system framework - Implemented some fault handling features for reliability/robustness - Created a number guessing game with a resilient central node and subnode architecture - Wrote simulation of ORBIT distributed environment using Docker

Week 4

​Week 4 Slides

L2H	PCA
- Read the L2H paper	- Read the PCA paper

Useful Links:

For Reserving Nodes (Usually Weeks): ​https://www.orbit-lab.org/cPanel/controlPanel/start

Documentation/Notes: ​https://drive.google.com/drive/folders/15HJmhyTUdzQafzMtNqIhy9jgc2sllF35?usp=sharing

Gitlab: ​https://gitlab.orbit-lab.org/r3-25

Presentations: ​https://drive.google.com/drive/folders/1aThnKsOUpBFiJtRgjq9AjoppeDdHGuQy?usp=sharing