2022/09/20 by Shashi Raj Pandey, Pandey, Shashi Raj, Lam Duc Nguyen +3
Computer Science · #Computer Science and Game Theory (cs.GT) #Cryptography and Data Security #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2209.09775
openalex publication_date 2022/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation. However, it is challenging to convince clients for quality participation in FL due to the absence of: (i) full information on the client's data quality and properties; (ii) the value of client's data contributions; and (iii) the trusted mechanism for monetary incentive offers. This often leads to poor efficiency in training and communication. While several works focus on strategic incentive designs and client selection to overcome this problem, there is a major knowledge gap in terms of an overall design tailored to the foreseen digital economy, including Web 3.0, while simultaneously meeting the learning objectives. To address this gap, we propose a contribution-based tokenized incentive scheme, namely FedToken, backed by blockchain technology that ensures fair allocation of tokens amongst the clients that corresponds to the valuation of their data during model training. Leveraging the engineered Shapley-based scheme, we first approximate the contribution of local models during model aggregation, then strategically schedule clients lowering the communication rounds for convergence and anchor ways to allocate affordable tokens under a constrained monetary budget. Extensive simulations demonstrate the efficacy of our proposed method.