PABP: An Approximately Truthful Auction Mechanism for Federated Learning with Bid Privacy Protection

Auction mechanisms incentivize device participation in federated learning (FL), but existing designs typically require devices to disclose their private bids to the server. Protecting these bids is challenging because perturbed bids can distort allocation and payment decisions, thereby undermining economic incentives. We propose PABP, an approximately truthful auction mechanism that jointly designs bid perturbation, device selection, resource allocation, and payment determination. Each device locally perturbs its bid with Gaussian noise, and the server makes decisions using only the perturbed bids. PABP further approximates the theoretical Myerson payment under noisy bids, which provides $\epsilon$-approximate incentive compatibility and individual rationality. Experiments across standard benchmarks and diverse data settings show that PABP protects bid privacy while consistently outperforming representative baselines.