  Federated learning (FL) has recently gained significant momentum due to its
potential to leverage large-scale distributed user data while preserving user
privacy. However, the typical paradigm of FL faces challenges of both privacy
and robustness: the transmitted model updates can potentially leak sensitive
user information, and the lack of central control of the local training process
leaves the global model susceptible to malicious manipulations on model
updates. Current solutions attempting to address both problems under the
one-server FL setting fall short in the following aspects: 1) designed for
simple validity checks that are insufficient against advanced attacks (e.g.,
checking norm of individual update); and 2) partial privacy leakage for more
complicated robust aggregation algorithms (e.g., distances between model
updates are leaked for multi-Krum). In this work, we formalize a novel security
notion of aggregated privacy that characterizes the minimum amount of user
information, in the form of some aggregated statistics of users' updates, that
is necessary to be revealed to accomplish more advanced robust aggregation. We
develop a general framework PriRoAgg, utilizing Lagrange coded computing and
distributed zero-knowledge proof, to execute a wide range of robust aggregation
algorithms while satisfying aggregated privacy. As concrete instantiations of
PriRoAgg, we construct two secure and robust protocols based on
state-of-the-art robust algorithms, for which we provide full theoretical
analyses on security and complexity. Extensive experiments are conducted for
these protocols, demonstrating their robustness against various model integrity
attacks, and their efficiency advantages over baselines.
