 for SS An ML-based spectrum sensing system called Federated Incumbent Detection in CBRS (FaIR) was proposed in [185]. FL was adopted for ESCs to collaborate and train a data-driven ML model for IU detection with minimal communication overhead. Unlike a naive dis- tributed sensing and centralized model framework, their proposed method could exploit the spatial diversity of the ESCs and improve detection performance. Because the existing ESC-based methods had the potential to incur a high communica- tion overhead and lead to leakage of sensitive information, a compressed sensing (CS)-based FL framework was proposed in [202] for IU detection. To protect privacy, local learning models transmitted updating parameters instead of raw spectrum data to the central server. A Multiple Measurement Vector (MMV) CS model was further adopted to aggregate these parameters. Based on the aggregated parameters, the central server could gain a global learning model and send the global parameters back to local learning models. Their pro- posed framework could significantly improve communication and training efficiency while guaranteeing detection performance compared with the raw training sample method. 10.2.2.3 Channel Selection and Transaction In the database-assisted SS system, each user needs to choose a proper vacant channel in order to avoid severe interference with others. When economic approaches are adopted to model the SS system, idle channels can be traded as commodities. For this reason, channel selection and spectrum trade problems are critical to the database-assisted SS system, and many works based on game theory have been proposed to solve them. In [29], a database-assisted distributed white-space Access Point (AP) network design was studied. The cooperative channel selection problem was first considered to maximize sys- tem throughput, where all APs were owned by one network operator. A distributed channel selection problem was then formed between APs that belonged to different operators, and a non-cooperative state