Modulated learning for private and distributed regression with just a single sample per client device

ArXi:2605.07233v1 Announce Type: new This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world applications exist to this one sample per client setup up including learning from fitness trackers, data/app usage aggregators, body-worn sensing devices, and daily event monitors to name a few.