Adaptive Low-Rank Optimization for Federated Learning
Undergraduate thesis on DyLoRA for communication-efficient federated optimization.
This undergraduate thesis integrated Dynamic Low-Rank Adaptation into a federated learning pipeline to reduce communication cost while preserving model performance.
Highlights
- Integrated DyLoRA into a modular federated learning benchmark.
- Reduced communication cost by 80% without loss of accuracy.
- Improved training stability across clients through reproducible PyTorch experiments.
- Received 85/100, High Distinction (Niu, 2025).
References
2025
- Adaptive Low-Rank Optimization for Federated Learning2025High Distinction, 85/100