Federated Learning Uses The Data Right on Our Devices

An approach called federated learning trains machine learning models on devices like smartphones and laptops, rather than requiring the transfer of private data to central servers. The biggest benchmarking data set to date for a machine learning technique designed with data privacy in mind is now available open source. "By training in-situ on data where it is generated, we can train on larger real-world data," explains Fan Lai, a doctoral student in computer science and engineering at the University of Michigan, who presents the FedScale training environment at the International Conference on Machine Learning this week. A paper on the work is available on ArXiv. "This also allows us to mitigate privacy risks and high communication and storage costs associated with collecting the raw data from end-user devices into the cloud," Lai says.