Please refer to the enclosed pdf.
Cross-silo federated learning (FL) enables organizations (e.g., financial or medical) to collaboratively train a machine learning model by aggregating local gradient updates from each client without sharing privacy-sensitive data. To ensure no update …
The remarkable advances of Machine Learning (ML) have spurred an increasing demand for ML-as-a-Service on public cloud - developers train and publish ML models as online services to provide low-latency inference for dynamic queries. The primary …
Machine-Learning-as-a-Service (MLaaS) enables practitioners and AI service providers to train and deploy ML models in the cloud using diverse and scalable compute resources. A common problem for MLaaS users is to choose from a variety of training …
The advances of Machine Learning (ML) have sparked a growing demand of ML-as-a-Service, developers train ML models and publish them in the cloud as online services to provide low-latency inference at scale. The key challenge of ML model serving is to …
Large machine learning models are typically trained in parallel and distributed environments. The model parameters are iteratively refined by multiple worker nodes in parallel, each processing a subset of the training data. In practice, the training …