Rademics Research Institute

Abstract

The rapid integration of IIoT in power electronics has transformed industrial automation, enabling real-time monitoring, predictive maintenance, and intelligent decision-making. The distributed nature of IIoT-enabled power electronics introduces significant challenges in anomaly detection, including data heterogeneity, privacy concerns, and computational limitations of edge devices. Traditional centralized learning approaches are inefficient in handling these constraints, necessitating the adoption of decentralized learning paradigms. Federated Learning (FL) has emerged as a transformative approach, enabling collaborative model training across edge devices while preserving data privacy. FL faces challenges such as communication overhead, resource constraints, and performance degradation due to non-independent and identically distributed (non-IID) data. To address these limitations, Transfer Learning (TL) was integrated with FL to enhance model adaptability, enabling efficient knowledge transfer across different industrial environments and reducing dependency on extensive labeled datasets.

This book chapter presents a comprehensive study on the integration of FL and TL for distributed anomaly detection in IIoT-enabled power electronics. The research explores optimization techniques for scalable FL deployment, including low-latency model aggregation, edge-to-cloud collaboration, and privacy-preserving secure model aggregation using Secure Multi-Party Computation (SMPC). The role of meta-learning in improving FL model generalization for handling heterogeneous data was analyzed. To address computational inefficiencies, the study examines Federated Knowledge Distillation (FKD) as a lightweight learning approach that minimizes resource consumption while maintaining high anomaly detection accuracy. The findings highlight the advantages of hybrid FL-TL frameworks in enhancing fault diagnosis, reducing communication overhead, and ensuring energy-efficient real-time anomaly detection. The proposed approach strengthens the reliability and security of industrial automation by providing a scalable and adaptive learning framework for power electronics systems. Future research directions include optimizing FL-TL integration for dynamic industrial environments, developing energy-efficient federated architectures, and enhancing privacy-preserving techniques for large-scale IIoT networks.

Introduction

The rapid expansion of the IIoT has significantly enhanced automation, predictive maintenance, and operational efficiency in power electronics [1]. Modern industrial systems rely on IIoT-enabled power electronic components, such as converters, inverters, and motor drives, to ensure energy efficiency and reliability [2]. The increasing complexity of these interconnected devices introduces challenges in detecting and mitigating anomalies that could lead to system failures, increased downtime, and operational inefficiencies [3,4]. Traditional anomaly detection models rely on centralized data processing, which poses critical challenges related to data privacy, high communication overhead, and computational inefficiencies. With the growing adoption of edge computing in industrial automation, there was a pressing need for decentralized learning approaches that enable real-time anomaly detection while preserving data privacy and optimizing resource utilization [5].

Federated Learning (FL) has emerged as a transformative paradigm that allows multiple edge devices to collaboratively train a global model without sharing raw data [6-9]. This decentralized approach ensures that sensitive industrial data remains localized, reducing security vulnerabilities while enabling continuous model updates across distributed systems. FL encounters several challenges, including communication constraints, non-independent and identically distributed (non-IID) data across edge devices, and the high computational costs associated with iterative model training [10]. These challenges hinder the effectiveness of FL-based anomaly detection in power electronics, necessitating the development of advanced optimization techniques to enhance model efficiency, adaptability, and scalability [11]. Addressing these limitations was crucial for enabling real-time fault diagnosis and predictive maintenance in IIoT-enabled industrial environments [12].