  Wireless Federated Learning (FL) is an emerging distributed machine learning
paradigm, particularly gaining momentum in domains with confidential and
private data on mobile clients. However, the location-dependent performance, in
terms of transmission rates and susceptibility to transmission errors, poses
major challenges for wireless FL's convergence speed and accuracy. The
challenge is more acute for hostile environments without a metric that
authenticates the data quality and security profile of the clients. In this
context, this paper proposes a novel risk-aware accelerated FL framework that
accounts for the clients heterogeneity in the amount of possessed data,
transmission rates, transmission errors, and trustworthiness. Classifying
clients according to their location-dependent performance and trustworthiness
profiles, we propose a dynamic risk-aware global model aggregation scheme that
allows clients to participate in descending order of their transmission rates
and an ascending trustworthiness constraint. In particular, the transmission
rate is the dominant participation criterion for initial rounds to accelerate
the convergence speed. Our model then progressively relaxes the transmission
rate restriction to explore more training data at cell-edge clients. The
aggregation rounds incorporate a debiasing factor that accounts for
transmission errors. Risk-awareness is enabled by a validation set, where the
base station eliminates non-trustworthy clients at the fine-tuning stage. The
proposed scheme is benchmarked against a conservative scheme (i.e., only
allowing trustworthy devices) and an aggressive scheme (i.e., oblivious to the
trust metric). The numerical results highlight the superiority of the proposed
scheme in terms of accuracy and convergence speed when compared to both
benchmarks.
