Federated Learning for Cross-Location Data Collaborative Training

Abstract

This paper presents a novel approach to collaborative model training leveraging federated learning, specifically designed for scenarios involving geographically dispersed data sources. The core challenge addressed is the 'data island' problem, where valuable data remains siloed due to logistical, regulatory, or competitive constraints. Our proposed algorithm, termed Federated Cross-Location Collaborative Training (FCCCT), utilizes the principles of federated learning to enable collaborative model training without direct data sharing. The system operates by iteratively sharing model parameters and gradients between participating data sources. This allows for the construction of a more robust and accurate global model while preserving the privacy of each individual dataset. We demonstrate the effectiveness of FCCCT through a theoretical analysis and outline a practical implementation framework. The key contributions of this work are the integration of federated learning with cross-location data synergy, providing a scalable and privacy-preserving solution for training high-performance models on distributed data. The presented framework offers a significant advancement in addressing the limitations of traditional collaborative learning methods.