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.
Still a new technology, federated learning relies on an algorithm that serves as a centralized coordinator. It delivers the model to the devices, trains it locally on the relevant user data, and then brings each partially trained model back and uses them to generate a final global model.
For a number of applications, this workflow provides an added data privacy and security safeguard. Messaging apps, health care data, personal documents, and other sensitive but useful training materials can improve models without fear of data center vulnerabilities.
In addition to protecting privacy, federated learning could make model training more resource-efficient by cutting down and sometimes eliminating big data transfers, but it faces several challenges before it can be widely used. Training across multiple devices means that there are no guarantees about the computing resources available, and uncertainties like user connection speeds and device specs lead to a pool of data options with varying quality.
“Federated learning is growing rapidly as a research area,” says Mosharaf Chowdhury, associate professor of computer science and engineering. “But most of the work makes use of a handful of data sets, which are very small and do not represent many aspects of federated learning.”
And this is where FedScale comes in. The platform can simulate the behavior of millions of user devices on a few GPUs and CPUs, enabling developers of machine learning models to explore how their federated learning program will perform without the need for large-scale deployment. It serves a variety of popular learning tasks, including image classification, object detection, language modeling, speech recognition, and machine translation.
“Anything that uses machine learning on end-user data could be federated,” Chowdhury says. “Applications should be able to learn and improve how they provide their services without actually recording everything their users do.”
The authors specify several conditions that must be accounted for to realistically mimic the federated learning experience: heterogeneity of data, heterogeneity of devices, heterogeneous connectivity and availability conditions, all with an ability to operate at multiple scales on a broad variety of machine learning tasks. FedScale’s data sets are the largest released to date that cater specifically to these challenges in federated learning, according to Chowdhury.
“Over the course of the last couple years, we have collected dozens of data sets. The raw data are mostly publicly available, but hard to use because they are in various sources and formats,” Lai says. “We are continuously working on supporting large-scale on-device deployment, as well.”
The FedScale team has also launched a leaderboard to promote the most successful federated learning solutions trained on the university’s system.
The National Science Foundation and Cisco supported the work.
Source: Zachary Chamption for University of Michigan
