FedScrap: Layer-Wise Personalized Federated Learning for Scrap Detection

Abstract

Scrap steel inspection is a critical entry point for connecting the smelting process to the industrial internet, with its security and privacy being of vital importance. Current advancements in scrap steel inspection involve collecting scattered data through the industrial internet, then utilizing them to train machine learning models for distributed classification. However, this detection method exposes original scrap steel data directly to the industrial internet, making it susceptible to interception by attackers, who can potentially obtain sensitive information. This paper presents a layer-wise personalized federated framework for scrap steel detection, termed FedScrap, which leverages federated learning (FL) to coordinate decentralized and heterogeneous scrap steel data while ensuring data privacy protection. The key challenge that FedScrap confronts is the heterogeneity of scrap steel data distributed across the network, which complicates the task of effectively integrating these data into a single detection model constructed via FL. To address this challenge, FedScrap employs a self-attention mechanism to aggregate personalized models for each layer of every client, focusing on the most relevant models to their specific data. By assigning higher attention scores to more relevant models, it achieves more accurate aggregation weights during the model aggregation process. To validate the efficacy of the proposed method, a dataset of scrap images was collected from a steel mill, and the results demonstrate that FedScrap achieves accurate classification of distributed scrap data with an impressive accuracy rate of 90%.