  Federated learning has recently emerged as a paradigm promising the benefits
of harnessing rich data from diverse sources to train high quality models, with
the salient features that training datasets never leave local devices. Only
model updates are locally computed and shared for aggregation to produce a
global model. While federated learning greatly alleviates the privacy concerns
as opposed to learning with centralized data, sharing model updates still poses
privacy risks. In this paper, we present a system design which offers efficient
protection of individual model updates throughout the learning procedure,
allowing clients to only provide obscured model updates while a cloud server
can still perform the aggregation. Our federated learning system first departs
from prior works by supporting lightweight encryption and aggregation, and
resilience against drop-out clients with no impact on their participation in
future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency
optimization in the ciphertext domain and the support of security against an
actively adversarial cloud server, which we also fully explore in this paper
and provide effective and efficient mechanisms. Extensive experiments over
several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system
achieves accuracy comparable to the plaintext baseline, with practical
performance.
