A simulation platform for auction-based federated learning - Tang, Daryl Jun Da (Nanyang Technological University, 2024)
Federated learning (FL) is a vast field of research that is concerned with distributed training of machine learning models while adhering to laws governing data privacy. Notwithstanding its benefits over traditional centralised machine learning, FL itself is augmented by methods introducing the concepts of incentives and reputation among the data owners (DO’s) and data consumers (DC’s) participating in FL. Indeed, research into Auction-based Federated Learning (AFL) delves into the various incentive mechanisms to aid in goal alignment of auctioneers, DO’s, and DC’s. AFL promises to retain the perks of privacy preserving distributed model training without the drawbacks of communication overheads and data which is non-independent and identically distributed (non-IID problem). In reverse-AFL, a single DC recruits several DO’s for model training, providing a bounty for clients contributing the most significant data and model advancements.