  In this paper, we study the setting in which data owners train machine
learning models collaboratively under a privacy notion called joint
differential privacy [Kearns et al., 2018]. In this setting, the model trained
for each data owner $j$ uses $j$'s data without privacy consideration and other
owners' data with differential privacy guarantees. This setting was initiated
in [Jain et al., 2021] with a focus on linear regressions. In this paper, we
study this setting for stochastic convex optimization (SCO). We present an
algorithm that is a variant of DP-SGD [Song et al., 2013; Abadi et al., 2016]
and provides theoretical bounds on its population loss. We compare our
algorithm to several baselines and discuss for what parameter setups our
algorithm is more preferred. We also empirically study joint differential
privacy in the multi-class classification problem over two public datasets. Our
empirical findings are well-connected to the insights from our theoretical
results.
