  Despite the impressive progress of general face detection, the tuning of
hyper-parameters and architectures is still critical for the performance of a
domain-specific face detector. Though existing AutoML works can speedup such
process, they either require tuning from scratch for a new scenario or do not
consider data privacy. To scale up, we derive a new AutoML setting from a
platform perspective. In such setting, new datasets sequentially arrive at the
platform, where an architecture and hyper-parameter configuration is
recommended to train the optimal face detector for each dataset. This, however,
brings two major challenges: (1) how to predict the best configuration for any
given dataset without touching their raw images due to the privacy concern? and
(2) how to continuously improve the AutoML algorithm from previous tasks and
offer a better warm-up for future ones? We introduce "HyperFD", a new
privacy-preserving online AutoML framework for face detection. At its core
part, a novel meta-feature representation of a dataset as well as its learning
paradigm is proposed. Thanks to HyperFD, each local task (client) is able to
effectively leverage the learning "experience" of previous tasks without
uploading raw images to the platform; meanwhile, the meta-feature extractor is
continuously learned to better trade off the bias and variance. Extensive
experiments demonstrate the effectiveness and efficiency of our design.
