Economics Model for Federated LLM Training via Blockchain

Soumyadeep Das, Madhusudan Singh

Keywords: Federated Learning, Large Language Models, Blockchain, Smart Contracts, Tokenized Incentives, Shapley Value Allocation, Game Theory in AI, Decentralized Governance

Abstract:

This article introduces a decentralized economic framework to support
collaborative training of Large Language Models (LLMs) by
mitigating the escalating computational expenses and data privacy
constraints characteristic of centralized AI development. Federated
Learning (FL) is employed to enable privacy-preserving, distributed
model training, while blockchain technologies are leveraged to
provide transparency, accountability, and trust among mutually untrusted
stakeholders. From an economic standpoint, the central objective
is the design of a fair, incentive-compatible, and cost-efficient
mechanism that sustains long-term participation in federated LLM
training. The proposed framework integrates game-theoretic modeling
with smart contracts to quantify participant contributions and
determine corresponding rewards. A Shapley value–based contribution
assessment scheme is adopted to promote allocative fairness,
discourage free-riding behavior, and align individual incentives
with improvements in the global model’s performance. In addition,
a tokenized incentive layer is introduced to enable decentralized
governance and verifiable, programmatic reward distribution. Preliminary
simulation results suggest enhanced fairness in contribution
evaluation, increased stability of participant engagement,
and reduced concentration of computational and financial burdens,
thereby demonstrating the economic viability and scalability of the
proposed approach.

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Submission Number: 8