  Federated edge learning (FEL) is a promising paradigm of distributed machine
learning that can preserve data privacy while training the global model
collaboratively. However, FEL is still facing model confidentiality issues due
to eavesdropping risks of exchanging cryptographic keys through traditional
encryption schemes. Therefore, in this paper, we propose a hierarchical
architecture for quantum-secured FEL systems with ideal security based on the
quantum key distribution (QKD) to facilitate public key and model encryption
against eavesdropping attacks. Specifically, we propose a stochastic resource
allocation model for efficient QKD to encrypt FEL keys and models. In FEL
systems, remote FEL workers are connected to cluster heads via quantum-secured
channels to train an aggregated global model collaboratively. However, due to
the unpredictable number of workers at each location, the demand for secret-key
rates to support secure model transmission to the server is unpredictable. The
proposed systems need to efficiently allocate limited QKD resources (i.e.,
wavelengths) such that the total cost is minimized in the presence of
stochastic demand by formulating the optimization problem for the proposed
architecture as a stochastic programming model. To this end, we propose a
federated reinforcement learning-based resource allocation scheme to solve the
proposed model without complete state information. The proposed scheme enables
QKD managers and controllers to train a global QKD resource allocation policy
while keeping their private experiences local. Numerical results demonstrate
that the proposed schemes can successfully achieve the cost-minimizing
objective under uncertain demand while improving the training efficiency by
about 50\% compared to state-of-the-art schemes.
