Trustworthy Federated Fine-Tuning for Industrial Chains Demand Forecasting

Abstract

Demand forecasting is crucial for the robust development of industrial chains, given the direct impact of consumer market volatility on production planning. However, in the intricate industrial chain environment, limited accessible data from independent production entities poses challenges in achieving high performances and precise predictions for future demand. Centralized training using machine learning modeling on data from multiple production entities is a potential solution, yet issues like consumer privacy, industry competition, and data security hinder practical machine learning implementation. This research introduces an innovative distributed learning approach, utilizing privacy-preserving federated learning techniques to enhance time-series demand forecasting for multiple entities pertaining to industrial chains. Our approach involves several key steps, including federated learning among entities in the industrial chain on a blockchain platform, ensuring the trustworthiness of the computation process and results. Leveraging Pre-training Models (PTMs) facilitates federated fine-tuning among production entities, addressing model heterogeneity and minimizing privacy breach risks. A comprehensive comparison study on various federated learning demand forecasting models on data from two real-world industry chains demonstrates the superior performance and enhanced security of our developed approach.