# Survey of Artificial Intelligence Model Marketplace
## 4 Model Pricing and Trading
### Model Pricing Schemes
#### 4.1.2 Pricing Techniques (X-Axis)
The proposed flexible and extensible auction-based pricing mechanism compensates data owners based on the performance improvements achieved in model training. Furthermore, prototypes were utilized to demonstrate the extensibility of the auction scheme. These include \(K\)-winner federated learning (an auction mechanism that ensures truthfulness and individual rationality with a fixed number of winners), exponential mechanism (an approximate truthful auction that uses stochastic price selection to maximize data owner's profit), and random sampling (a dual-price auction mechanism that ensures truthfulness by partitioning buyers into groups and uses separate pricing for each group). [PERSON] et al. [95] proposed a two-tiered multi-attribute reverse auction framework. In their work, the first tier optimizes the data owners' selection process for model training. Similarly, the second tier (the pricing framework) leverages Nash equilibrium principles to fairly compensate the selected data owners based on the level of their data quality, computational resources utilized, and quality of the trained models. It maximizes the broker's revenue by strategically setting preference weights in the scoring function to align with the market conditions to guarantee profitability. [PERSON] et al. [96] proposed to integrate auction theory with reinforcement learning (RL) in model marketplaces. The proposed auction mechanism utilizes performance gain as the main criterion to determine pricing. Buyers bid based on the expected performance improvement a model can provide, while the payment to the data owners is proportional to the performance gain their models offer to the buyers. The RL-based allocation function acts as a market operation solver that guides the broker in ranking buyers based on their bids to determine the winning buyers, optimize the auction process to maximize trading volumes, and adapt to dynamic market conditions. As for the privacy-based approach, data owners submit bids based on their privacy budget and receive compensation based on the level of their privacy loss. Related to this, [PERSON] et al. [92] proposed _\"DM-Regregnet\"_, an auction mechanism that determines the level of privacy disclosed in model training and compensates the data owners accordingly. In their method, data owners report their privacy valuations, which reflect their willingness to accept privacy loss in exchange for compensation. Each owner specifies a privacy budget (the maximum privacy loss they will tolerate). The auction mechanism allows data owners to bid on their privacy loss, with the broker determining the privacy parameter and compensation based on these bids. _\"DM-Regregnet\"_ works with an aggregation mechanism _\"OptAggr\"_ to optimize the utility of the global gradient. This involves determining the optimal level of privacy loss for each data owner to maximize the accuracy of the aggregated model. Also, [PERSON] et al. [97] integrated privacy-preserving techniques (differential privacy) with an auction-based model to facilitate secure and efficient model sharing in federated model trading marketplaces. Their approach transforms model sharing into private auction models; symmetric homomorphic encryption is employed to securely sort bids and calculate prices without revealing the actual bid values to the parties involved in the auction, attribute-based encryption and the interplanetary file system ensure that only the winner has access to the shared model. In another study, [PERSON] et al. [91] proposed to balance the trade-offs between the data owner's privacy protection, buyer's model quality assurance, and broker's profit maximization. In their method, the broker optimizes the selection of data owners for model training, strategically sets prices for the different model versions trained, and then employs a two-layer optimization framework to maximize her revenue (profit). **Other Techniques:** These techniques range from noise injection (Gaussian and Laplacian), differential privacy, Shapley value, genetic algorithm, etc.