Federated Machine Learning: A Scalable, Privacy-Preserving Approach Ready for Production Environments
Federated machine learning has created new possibilities for privacy-preserving data analysis. This is a thriving area of research that primarily focuses on the algorithmic details and communication overheads necessary to train accurate models. Despite significant progress in the field, production-grade federated machine learning frameworks that address essential properties such as security, data privacy, scalability, fault tolerance, and performance in geographically distributed settings have yet to be available to ML engineers.
This talk will highlight the core concepts and features necessary for developing federated learning platforms for production environments at Enterprises. Furthermore, it will briefly cover two active use cases demonstrating the potential of regulated datasets in geographically distributed settings.
Salman holds a Ph.D. in scientific computing and is an expert on federated machine learning (FedML), scientific data management, scalability, and distributed computing infrastructure performance (DCI). He is the Chief Technology Officer (CTO) at Scaleout Systems and an Associate Professor at Uppsala University, where he researches e-infrastructures.
