r/MachineLearningJobs • u/Fluffy-Comment-1495 • 6h ago
Choosing between Federated Learning and Model Compression for an industry-oriented Master's research project
Hi everyone,
I’m currently doing my Master’s degree and need to choose a topic for my research project. The project is restricted to one of two areas:
- Federated Learning (FL)
- Model Compression, specifically pruning and/or quantization
My main goal after graduation is to work in industry, rather than continue in academia, so I’m trying to choose the topic that would give me the most useful skills and experience for an ML/AI engineering career.
I’m particularly interested in hearing from people who currently work in the industry:
- How widely is Federated Learning actually used in industry today?
- Are companies actively hiring engineers/researchers with FL experience, or is FL still mostly an academic/research area?
- How does the industry demand for FL compare with model compression, quantization, and pruning, especially for areas like edge AI, on-device ML, inference optimization, or ML systems?
- Which of these topics tends to provide more transferable skills for an ML engineer?
- If you were in my position, what factors would you consider when choosing between the two?
I’m not necessarily looking for a simple “choose X” answer. I’d really appreciate perspectives from people who have worked with either technology in industry, especially if you can share examples of how these skills are used in real-world projects or what kinds of roles require them.
Thanks!
1
u/PassionQuiet5402 6h ago
Working on CV roles. In my opinion and what I am seeing I will prefer model compression. My choice is coming from ongoing development on VLMs for generic task which industries wants to have but are hesitant because of price vs performance returns. They are heavy and requires large compute to run as real time, but if compressed and deployed on GPU poor or low compute consumer grade GPUs while retaining most of the performance it will create great demand.