Federated learning is a great tool for training Artificial Intelligence (AI) systems while protecting data privacy, but the amount of data traffic involved has made it unwieldy for systems that include wireless devices. A new technique uses compression to drastically reduce the size of data transmissions, creating additional opportunities for AI training on wireless technologies.
Federated learning is a form of machine learning involving multiple devices, called clients. Each of the clients is trained using different data and develops its own model for performing a specific task. The clients then send their models to a centralised server.
The centralised server draws on each of those models to create a hybrid model, which performs better than any of the other models on their own. The central server then sends this hybrid model back to each of the clients. The entire process is then repeated, with each iteration leading to model updates that ultimately improve the system’s performance.
One of the advantages of federated learning is that it can allow the overall AI system to improve its performance without compromising the privacy of the data being used to train the system. For example, you could draw on privileged patient data from multiple hospitals in order to improve diagnostic AI tools, without the hospitals having access to data on each other’s patients
– Chau-Wai Wong, Assistant Professor, Electrical and Computer Engineering, North Carolina State…
