3 papers
cs.LG2026
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
Zhongpeng Qi, Jun Zhang, Wei Li +1
Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and in…
cs.LG2024
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
Xiaolu Wang, Yuchang Sun, Hoi-To Wai +1
We consider the distributed learning problem with data dispersed across multiple workers under the orchestration of a central server. Asynchronous Stochastic Gradient Descent (SGD)…
cs.LG2024
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Xiaolu Wang, Zijian Li, Shi Jin +1
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at…