  AI-enabled critical infrastructures (ACIs) integrate artificial intelligence
(AI) technologies into various essential systems and services that are vital to
the functioning of society, offering significant implications for efficiency,
security and resilience. While adopting decentralized AI approaches (such as
federated learning technology) in ACIs is plausible, private and sensitive data
are still susceptible to data reconstruction attacks through gradient
optimization. In this work, we propose Compressed Differentially Private
Aggregation (CDPA), a leakage-resilient, communication-efficient, and
carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a
novel random bit-flipping mechanism as its primary innovation. This mechanism
first converts gradients into a specific binary representation and then
selectively flips masked bits with a certain probability. The proposed
bit-flipping introduces a larger variance to the noise while providing
differentially private protection and commendable efforts in energy savings
while applying vector quantization techniques within the context of federated
learning. The experimental evaluation indicates that CDPA can reduce
communication cost by half while preserving model utility. Moreover, we
demonstrate that CDPA can effectively defend against state-of-the-art data
reconstruction attacks in both computer vision and natural language processing
tasks. We highlight existing benchmarks that generate 2.6x to over 100x more
carbon emissions than CDPA. We hope that the CDPA developed in this paper can
inform the federated AI-enabled critical infrastructure of a more balanced
trade-off between utility and privacy, resilience protection, as well as a
better carbon offset with less communication overhead.
