3 papers
cs.LG2026
Robust Federated Learning Under Real-World Client Churn
Dhruv Garg, Neha Lakhani, Debopam Sanyal +3
Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the comple…
cs.LG2026
KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs
Debopam Sanyal, Anantharaman Iyer, Alind Khare +5
Given the wide range of deployment targets, flexible model selection is essential for optimizing performance within a given compute budget. Recent work demonstrates that stitching…
cs.CV2024
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal +4
CNNs are increasingly deployed across different hardware, dynamic environments, and low-power embedded devices. This has led to the design and training of CNN architectures with th…