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To prevent implicit privacy disclosure in sharing gradients among data owners(DOs) under federated learning (FL), differential privacy (DP) and its variantshave become a common practice to offer formal privacy guarantees with lowoverheads. However, individual DOs generally tend to inject larger DP noisesfor stronger privacy provisions (which entails severe degradation of modelutility), while the curator (i.e., aggregation server) aims to minimize theoverall effect of added random noises for satisfactory model performance. Toaddress this conflicting goal, we propose a novel dynamic privacy pricing(DyPP) game which allows DOs to sell individual privacy (by lowering the scaleof locally added DP noise) for differentiated economic compensations (offeredby the curator), thereby enhancing FL model utility. Consideringmulti-dimensional information asymmetry among players (e.g., DO's datadistribution and privacy preference, and curator's maximum affordable payment)as well as their varying private information in distinct FL tasks, it is hardto directly attain the Nash equilibrium of the mixed-strategy DyPP game.Alternatively, we devise a fast reinforcement learning algorithm with twolayers to quickly learn the optimal mixed noise-saving strategy of DOs and theoptimal mixed pricing strategy of the curator without prior knowledge ofplayers' private information. Experiments on real datasets validate thefeasibility and effectiveness of the proposed scheme in terms of fasterconvergence speed and enhanced FL model utility with lower payment costs.

Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning