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Adopting honeypot defenses is a promising technology for protecting the industrial Internet of Things (IIoT), particularly the Advanced Metering Infrastructure (AMI). The effectiveness of AMI defense is entirely reliant on the deployment of honeypots by small-scale power suppliers (SPSs) and then sharing the defense data with traditional power retailers (TPRs) to build anomaly detectors. TPR encourages the SPSs to share their collected honeypot logs by designing proper rewards. However, TPRs cannot confirm the validity of the shared defense data unless they have access to SPSs' private data, compromising their privacy since SPSs may be reluctant to disclose their private collected data. In addition, the honeypot logs are large, which increases the sharing costs. Federated Learning (FL), as a promising privacy-preserving machine learning technique, can solve these problems. Yet, the conventional FL algorithm …