  In contemporary edge computing systems, decentralized edge nodes aggregate
unprocessed data and facilitate data analytics to uphold low transmission
latency and real-time data processing capabilities. Recently, these edge nodes
have evolved to facilitate the implementation of distributed machine learning
models, utilizing their computational resources to enable intelligent
decision-making, thereby giving rise to an emerging domain referred to as edge
intelligence. However, within the realm of edge intelligence, susceptibility to
numerous security and privacy threats against machine learning models becomes
evident. This paper addresses the issue of membership inference leakage in
distributed edge intelligence systems. Specifically, our focus is on an
autonomous scenario wherein edge nodes collaboratively generate a global model.
The utilization of membership inference attacks serves to elucidate the
potential data leakage in this particular context. Furthermore, we delve into
the examination of several defense mechanisms aimed at mitigating the
aforementioned data leakage problem. Experimental results affirm that our
approach is effective in detecting data leakage within edge intelligence
systems, and the implementation of our defense methods proves instrumental in
alleviating this security threat. Consequently, our findings contribute to
safeguarding data privacy in the context of edge intelligence systems.
