  Honeypot technologies provide an effective defense strategy for the
Industrial Internet of Things (IIoT), particularly in enhancing the Advanced
Metering Infrastructure's (AMI) security by bolstering the network intrusion
detection system. For this security paradigm to be fully realized, it
necessitates the active participation of small-scale power suppliers (SPSs) in
implementing honeypots and engaging in collaborative data sharing with
traditional power retailers (TPRs). To motivate this interaction, TPRs
incentivize data sharing with tangible rewards. However, without access to an
SPS's confidential data, it is daunting for TPRs to validate shared data,
thereby risking SPSs' privacy and increasing sharing costs due to voluminous
honeypot logs. These challenges can be resolved by utilizing Federated Learning
(FL), a distributed machine learning (ML) technique that allows for model
training without data relocation. However, the conventional FL algorithm lacks
the requisite functionality for both the security defense model and the rewards
system of the AMI network. This work presents two solutions: first, an enhanced
and cost-efficient FedAvg algorithm incorporating a novel data quality measure,
and second, FedPot, the development of an effective security model with a fair
incentives mechanism under an FL architecture. Accordingly, SPSs are limited to
sharing the ML model they learn after efficiently measuring their local data
quality, whereas TPRs can verify the participants' uploaded models and fairly
compensate each participant for their contributions through rewards. Simulation
results, drawn from realistic mircorgrid network log datasets, demonstrate that
the proposed solutions outperform state-of-the-art techniques by enhancing the
security model and guaranteeing fair reward distributions.
