  Federated learning (FL) is an emerging paradigm for training machine learning
models using possibly private data available at edge devices. The distributed
operation of FL gives rise to challenges that are not encountered in
centralized machine learning, including the need to preserve the privacy of the
local datasets, and the communication load due to the repeated exchange of
updated models. These challenges are often tackled individually via techniques
that induce some distortion on the updated models, e.g., local differential
privacy (LDP) mechanisms and lossy compression. In this work we propose a
method coined joint privacy enhancement and quantization (JoPEQ), which jointly
implements lossy compression and privacy enhancement in FL settings. In
particular, JoPEQ utilizes vector quantization based on random lattice, a
universal compression technique whose byproduct distortion is statistically
equivalent to additive noise. This distortion is leveraged to enhance privacy
by augmenting the model updates with dedicated multivariate privacy preserving
noise. We show that JoPEQ simultaneously quantizes data according to a required
bit-rate while holding a desired privacy level, without notably affecting the
utility of the learned model. This is shown via analytical LDP guarantees,
distortion and convergence bounds derivation, and numerical studies. Finally,
we empirically assert that JoPEQ demolishes common attacks known to exploit
privacy leakage.
