Cost-Efficient and Privacy-Preserving Distributed Learning: A Double Layer-Based Auction Design

The rise of artificial intelligence of things (AIoT) has enabled AI-powered services within wireless networks, relying on well-trained machine learning (ML) models. Distributed learning, such as federated learning (FL), allows smart devices (SDs) to collaborate on model training without sharing raw data, but privacy protection is still necessary to prevent potential information leakage from evolving attacks. Additionally, training efficiency is hampered by limited resources and selfishness of SDs. This paper considers a layered distributed learning scenario using a double-layer auction approach, where model users act as buyers, SDs act as data owners contributing their datasets, and edge layer nodes (ELNs) serve as model trainers providing computing resources. The differential privacy (DP) mechanism is utilized to add Gaussian noise to the trained models by the ELNs. Then, we formulate a joint optimization problem to optimize task assignment, data owners' sensing durations, and model trainers' local iterations and privacy budgets, aiming to maximize the utility of all participants while ensuring cost-effective and privacy-preserving distributed learning. We decompose the formulated problem into four sub-problems and design a layered algorithm to solve them and derive collaboration strategies. Simulation results validate the algorithm's performance and demonstrate the advantages of our proposed approach compared to benchmark schemes.