Differentially Private Federated Learning via Reconfigurable Intelligent Surface. (arXiv:2203.17028v1 [eess.SP])

Federated learning (FL), as a disruptive machine learning paradigm, enables
the collaborative training of a global model over decentralized local datasets
without sharing them. It spans a wide scope of applications from
Internet-of-Things (IoT) to biomedical engineering and drug discovery. To
support low-latency and high-privacy FL over wireless networks, in this paper,
we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL
system to alleviate the dilemma between learning accuracy and privacy. This is
achieved by simultaneously exploiting the channel propagation reconfigurability
with RIS for boosting the receive signal power, as well as waveform
superposition property with over-the-air computation (AirComp) for fast model
aggregation. By considering a practical scenario where high-dimensional local
model updates are transmitted across multiple communication blocks, we
characterize the convergence behaviors of the differentially private federated
optimization algorithm. We further formulate a system optimization problem to
optimize the learning accuracy while satisfying privacy and power constraints
via the joint design of transmit power, artificial noise, and phase shifts at
RIS, for which a two-step alternating minimization framework is developed.
Simulation results validate our systematic, theoretical, and algorithmic
achievements and demonstrate that RIS can achieve a better trade-off between
privacy and accuracy for over-the-air FL systems.