3 papers
stat.ML2026
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Arnab Auddy, Xiangni Peng, Subhadeep Paul
Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-offs among estimation accuracy…
cs.DC2025
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
Xian Peng, Xin Wu, Lianming Xu +2
Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edg…
cs.DC2024
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices
Li Wang, Liang Li, Lianming Xu +2
The distributed inference paradigm enables the computation workload to be distributed across multiple devices, facilitating the implementations of deep learning based intelligent s…