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Fusion of Federated Learning and Industrial Internet of Things: A Survey

Parimala M, Swarna Priya R M, Quoc-Viet Pham, Kapal Dev, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Thien Huynh-The

arXiv:2101.00798v1cs.NIcs.AI

TL;DR

IIoT applications generate huge, heterogeneous data and face privacy, security, and management challenges when data is centralized. This survey reviews federated learning integration across secure learning, data and resource management, and industrial applications, concluding that FL, IIoT, and privacy-preserving mechanisms can be consolidated within an Industry 4.0 paradigm.

  • Problem

    IIoT must manage huge, heterogeneous industrial data while protecting sensitive information and addressing security, resource, and data-management challenges.

  • Method

    The paper surveys FL and IIoT fundamentals, privacy-preserving ML/DL and blockchain models, heterogeneous-data handling, data and resource management, applications, challenges, and solutions.

  • Results

    The survey presents approaches for privacy-preserving learning, data and resource management, and FL applications in automobile and healthcare settings.

  • Takeaways & Limitations

    The paper consolidates IIoT, FL, privacy-preserving, data-management, and storage mechanisms under a common Industry 4.0 paradigm.

Abstract

from arXiv · show

Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributive and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning, deep learning and blockchain techniques for FL in secure IIoT. Further we analyze and summarize the ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in healthcare and automobile industry. Finally, we shed light on challenges, some possible solutions and potential directions for future research.

I. INTRODUCTION

The paper surveys federated learning for Industrial Internet of Things, motivated by privacy concerns and the need to use distributed device capabilities. It reviews fundamentals, security, data and resource management, applications, challenges, and research directions.

  • Motivation: Centralized AI training creates privacy concerns because users may not want to transmit local data to a central server.Centralized training also requires substantial cloud computing and storage, while IoT devices increasingly possess local capabilities.
  • Federated learning: Federated learning trains models on IoT devices and sends updated local models to centralized infrastructure instead of transmitting local data.This supports on-device learning and data privacy preservation.
  • Motivation: Integrating FL with IIoT is challenging because industrial applications demand stronger security, safety, reliability, robustness, resource management, and incentives.The paper identifies these challenges as more difficult in IIoT than in general IoT systems.
  • Scope and contributions: The paper surveys FL for IIoT applications, presenting fundamentals, state-of-the-art security and privacy, data management, resource management, applications, challenges, and open research issues.It positions the work as an integration-focused survey rather than separate surveys of FL and IIoT.

B. Paper Organization

The paper distinguishes federated learning from distributed learning and organizes its discussion around IIoT fundamentals, applications, management, security, and future challenges. It emphasizes that local training and parameter exchange address privacy and scalability concerns while supporting industrial intelligence.

  • Fundamentals of distributed learning: Traditional centralized machine learning struggles with the scalability and time-complexity demands of rapidly growing real-time industrial data.Large training datasets can exceed the practical limits of centralized processing.
  • Fundamentals of distributed learning: Distributed learning fragments data across nodes and executes algorithms in parallel, reducing training time and memory requirements.The paper describes horizontal and vertical fragmentation as two distribution approaches.
  • Fundamentals of distributed learning: Distributed learning remains limited by privacy, security, platform dependency, and the lack of standard evaluation measures.These limitations motivate the introduction of federated learning.
  • Fundamentals of federated learning: Federated learning trains models on decentralized data, exchanges model parameters periodically, and creates a global model by averaging local parameters.Training data remains on individual devices, while the global model resides at a server.
  • Fundamentals of federated learning: Unlike distributed learning, federated learning accommodates heterogeneous local dataset sizes and focuses on locally generated device data rather than mainly parallelizing computing power.The paper contrasts FL nodes with the typically data-center-based nodes used in distributed learning.
  • IIoT fundamentals: IIoT connects people, sensors, machines, and computers to support intelligent industrial operations, with applications spanning manufacturing, transport, healthcare, energy, and other sectors.Networked sensors can support prediction and automated decision-making, including anticipating machine breakdowns.

D. Motivations of Integrating Federated Learning with IIoT

Integrating FL with IIoT is motivated by security, privacy, communication, network-performance, and scalability needs. FL keeps industrial data local while exchanging model-derived updates, although privacy and security risks remain during update sharing.

  • Security and Data Privacy Preservation: FL avoids transferring sensitive IIoT datasets to a central location, preserving data privacy against potential hackers and intruders.
  • Reduced Communication Cost: FL reduces communication costs by sending summarized ML/DL results instead of transferring entire IIoT datasets to the cloud.
  • Improved Performance of the Network: FL can improve network performance because local model execution reduces traffic sent to the central location.
  • Scalability: FL supports scalability by training the central algorithm on summarized results from multiple edge devices rather than their full local datasets.
  • Security and Data Privacy Preservation: IIoT security concerns include eavesdroppers on communication channels and hackers at centralized servers.
  • Privacy-Preserving Mechanisms: Model updates can still leak sensitive information, so FL employs differential privacy, homomorphic encryption, and secure multiparty computation.

B. Federated Deep Learning in IIoT

Federated deep learning applies decentralized, optimized DL models to IIoT devices for Industry 4.0 intelligence. Blockchain can complement FL by validating and immutably recording model updates, but consensus and model protection remain security concerns.

  • Federated Deep Learning: Federated deep learning enables Industry 4.0 companies to deploy DL on IoT devices through an FL-based secure framework.DL is computationally expensive, making decentralized deployment with edge computing and IIoT a multidimensional challenge.
  • FDL Model: Client-side FDL trains compressed, quantized models on locally generated data, while the cloud integrates differentially derived updates from private networks.
  • Blockchain Integration: Blockchain can replace traditional central servers, store model updates immutably, and preserve tamper-resistant historical information.
  • Node Security: Blockchain node security uses permission control, validated update chains, and committee consensus to identify potentially malicious nodes.Malicious-node updates are ignored when constructing the global model.
  • Block Security: Block consensus is time-consuming to execute and broadcast, creating a need to reduce its cost.
  • Model Security: FL avoids exchanging raw client data, but centralized global models remain vulnerable to unauthorized hacking and require protection at both ends.
  • Blockchain-FL Workflow: The blockchain-FL workflow aggregates local updates across edge and fog nodes before smart-contract verification and tamper-proof on-chain storage.
  • Applications and Scalability: Blockchain-based FL has been applied in image processing, vehicular networks, healthcare, railway, and defense IIoT settings, with scalability required for real-world scenarios.

IV. DATA MANAGEMENT AND RESOURCE MANAGEMENT

IIoT produces heterogeneous, high-volume data that conventional management techniques struggle to acquire, store, and process. The surveyed approaches use edge, fog, cloud, architectural, middleware, indexing, and resource-management solutions, but scalable data management remains challenging.

  • Data Characteristics: IIoT data spans SCADA logs, business applications, RFID, wearables, sensors, web, media, and location sources, creating heterogeneous and huge datasets.
  • Data Characteristics: Traditional data-management techniques fail to handle the heterogeneous, high-volume data generated by interconnected physical, digital, and virtual objects.
  • Data Characteristics: IoE extends beyond IoT and IIoT, with data volumes exceeding terabytes and including structured, semi-structured, and unstructured forms.
  • Data Storage and Processing: Fog, cloud, and edge computing provide opportunities for storing and processing massive IIoT data and reacting at the right time.
  • Data Storage and Processing: A proposed IIoT data framework integrates preprocessing, retrieval, storage, and archiving across edge, proxy, and cloud servers.
  • Resource Management: Resource-management studies combine cloud, edge, and software-defined networking, although dynamic resource provision incurred high energy consumption.
  • Data Storage and Processing: Fuzzy Folded Bloom Filters compress two filters into one, storing more data elements while reducing decay and computational cost through double hashing.
  • Data Management Frameworks: An industrial data-management system monitors large real-time manufacturing data online to support decisions and emergency-event handling.

B. Federated Learning for Data Management

FL-based data management approaches address IIoT data-sharing, privacy, heterogeneity, and scalability challenges through blockchain, MPC, optimization, and edge-oriented coordination. The surveyed methods improve data handling and model or service efficiency, but no single architecture is specialized for a particular industrial sector.

  • FL-based IIoT methods address data-sharing and leakage concerns by coordinating learning without requiring centralized access to sensitive industrial data.Blockchain-FL integration is described as securing distributed data sharing, while FL avoids directly centralizing raw data.
  • Two-phase MPC aggregation protects sensitive data from multiple companies while achieving high accuracy with reduced communication cost and computation time.The method first elects a small committee and then provides MPC-based aggregation in a smart manufacturing IoT environment.
  • Blockchain and edge-intelligence scheduling improve the efficiency and service cost of distributed edge resources in IIoT networks beyond 5G.The scheme also includes transaction approval for accessing distributed edge resources.
  • PSO-assisted FL tunes local LSTM hyperparameters and reduces the number of parameter-identification rounds by 2% to 4%.PSO is used to set hyperparameters for locally available LSTM models in smart-city applications.
  • The surveyed algorithms can be adapted across industries, but no specific FL architecture is centered on a particular industrial sector.Adaptation depends on efficiency, device setup, end users, end servers, structure, and autonomy.

C. Federated Learning for Resource Management

The survey presents FL as a means to optimize communication, computation, task offloading, and resource allocation across heterogeneous IIoT environments. Reviewed approaches include analytical optimization, dispersed and industrial FL, blockchain-enabled coordination, federated reinforcement learning, and UAV systems.

  • Resource allocation: Wireless FL studies derive convergence expressions to optimize transmission power, resource-block allocation, and user selection under a single-base-station scenario.The formulation also minimizes the loss function for resource allocation and user selection.
  • Industrial adaptation: Deep-Q, dispersed FL, and Industrial FL address task offloading, resource optimization, and adaptation to diverse industrial operating conditions.Industrial FL trains and evaluates models while defining requirements and structured architectural workflows.
  • Secure coordination: Blockchain-based FL in digital-twin wireless networks balances learning accuracy with bandwidth allocation while improving reliability, security, and data privacy.Digital twins are used to mitigate unreliable communication between edge servers and users.
  • Federated reinforcement learning: Federated reinforcement learning supports dynamic resource allocation and network management through IIoT slicing and Deep Federated Q-Learning.The surveyed work also proposes multi-agent deep Q-learning approaches.
  • Edge scheduling: Cross-domain edge scheduling uses credit-differentiated transaction approval to improve flexibility and security at minimal service cost.The scheme targets heterogeneous IIoT resources and distributed edge services.
  • Resource optimization: FL resource management targets communication and computation resources needed for local learning, model updates, and global aggregation.Optimizing these resources significantly reduces the learning cost of FL models.

V. APPLICATIONS

Automotive IIoT combines connected machines, control systems, and FL models to support data-driven smart manufacturing while avoiding centralized data-sharing risks. The application requires privacy, reliability, low latency, and resource-aware processing because industrial devices and connected vehicles operate under constrained conditions.

  • Automotive context: Automotive IIoT connects computers, people, machines, and vehicles through data-driven algorithms for smart and sustainable production.Modern vehicular systems also connect vehicles and occupants, motivating FL-enabled Internet of Vehicles concepts.
  • Application constraints: Centralized training creates latency, privacy, and leakage concerns, while local-only training requires substantial resources on constrained IIoT devices.Industrial devices may have limited computation ability, local memory, and battery capacity.
  • FL workflow: An FL-enabled automotive model selects edge clients, uses machine and industry-partner data locally, and sends model updates to the cloud for aggregation.The process repeats until a global model reaches considerable accuracy, with local updates securely aggregated by weighted averaging.
  • Smart manufacturing: Smart manufacturing uses cooperative communication among automated machines and control systems to collect and analyze data for lower-cost production decisions.FL models support decisions about what action to take and when to perform it.

1) Requirements:

FL-enabled automotive IIoT requires coordinated data management, machine-to-machine communication, information quality, and scheduling. Its reported advantages are decentralized resource use, privacy protection, and reduced decision delay.

  • Requirements: Automotive FL systems require continuous monitoring and adaptation to support industrial operations.
  • Data management: Data management requires metadata about organizations and edge devices so selected FL clients can collaborate without sharing private raw data.Industrial clients regularly update models using local IIoT device data.
  • M2M communications: M2M communication lets automated machines exchange data and work collaboratively, requiring efficient routing in dense and mobile production environments.
  • Quality of Information (QoI): Quality of information reflects the diverse information reaching the global model through local-model aggregation and updates.Local training and evaluation expose the global model to varied industrial datasets.
  • Scheduling Algorithms: Scheduling algorithms prioritize new datasets, avoid repeated processing of older data, and reduce congestion during computation and task offloading.FL training can impose heavy loads on edge IIoT devices.
  • Advantages: Decentralized FL supports collaborative resource allocation, protects raw-data privacy, and reduces decision delay compared with traditional techniques.Blockchain and edge computing can be incorporated to support real-time industrial IoT systems.

3) Applications:

FL-enabled IIoT applications support privacy-preserving collaboration in vehicular, healthcare, and industrial settings. These systems train or update models locally while sharing model information rather than sensitive raw data.

  • Vehicular Applications: FL-enabled IoV trains and updates a global model in isolation, preserving user-data privacy while supporting collaborative autonomous vehicle applications.Applications include traffic signal control, navigation, autonomous driving, collision avoidance, and vehicle platooning.
  • Vehicular Applications: FL with V2V communication supports vehicle platooning by training a global model without data leakage while maintaining speed and inter-vehicle distance requirements.
  • Healthcare Applications: Healthcare applications use IIoT for remote patient monitoring, improved access to medical resources, and reduced pressure on healthcare systems.
  • Smart Healthcare Industry: In smart healthcare manufacturing, sensitive machine data remains private on local edge devices, where models analyze asset data and detect manufacturing anomalies.The described workflow begins with initial global-model data selection and local edge-device training.
  • Smart Healthcare Industry: Federated healthcare workflows repeatedly update a global model from local training, while service scheduling manages IIoT tasks and supports energy efficiency.The passage identifies FedAvg as an aggregation algorithm and describes repeated development of an accurate global model.

VI. CHALLENGES AND SOLUTIONS

IIoT deployment is constrained by heterogeneous architectures, high-volume streaming data, storage and retrieval requirements, interoperability, and privacy threats. Proposed responses emphasize data-management models, coordination across systems, and encryption-based protection.

  • Data Management: High-volume, high-velocity, and sometimes unstructured IIoT data must be stored, retrieved promptly, and protected during archiving.These requirements contribute to industries’ hesitation about real-time implementation.
  • Data Management: Efficient data-management models are proposed to improve storage, sharing, processing, analysis, and high-speed retrieval of sensitive industrial data.
  • Heterogeneity and Interoperability: IIoT systems combine heterogeneous devices, networks, servers, and vendors, making integration and interoperability challenging.The environments may include machines, robots, sensors, actuators, wired or wireless networks, 5G, and edge or cloud servers.
  • Privacy and Security: Privacy threats affect edge-to-cloud communication and model updates, including possible theft or tampering by malicious users.Fully Homomorphic Encryption is proposed to encrypt operations and balance privacy with accuracy.

D. Optimization

FL optimization must address constrained end-device resources, communication overhead, inference attacks, model poisoning, and data leakage. Proposed solutions combine model simplification, secure computation, differential privacy, anomaly detection, and robust aggregation.

  • Optimization: Limited memory, computation, power, and connectivity make model and communication optimization essential for efficient edge training.The proposed direction is to reduce computational complexity and communication overhead while balancing resource use and model performance.
  • Optimization: Secure aggregation and FHE-based MPC are proposed to protect updates while reducing communication rounds and overhead.Threshold FHE is identified as a way to reduce communication overhead.
  • Privacy Attacks: Differential privacy mitigates inference attacks by adding noise, requiring a balance between privacy protection and model accuracy.Domain-specific data augmentation is described as potentially limiting accuracy impact.
  • Model Security: Model poisoning and backdoor attacks remain difficult to address with a single defense, motivating combinations of anomaly detection, participant-level DP, and Byzantine-tolerant optimization.Secure aggregation can hide individual updates from the aggregator but does not provide one universal solution.
  • Data Security: Data leakage creates security and financial risks in distributed industrial data sharing, motivating secure transmission mechanisms.
  • Blockchain Solutions: Blockchain-based FL is proposed to improve trust management and secure sensitive-data transfer in distributed IIoT environments.

H. Data Privacy

The survey frames privacy as central to FL-enabled IIoT because industrial data and model updates may expose sensitive information. It reviews privacy-preserving sharing, communication-efficient training, encrypted mining, and anomaly detection approaches.

  • Data Privacy: IIoT data privacy includes global and local protection concerns because model updates may expose sensitive information during repeated training.
  • Blockchain-Based Privacy: PriModChain uses smart contracts and an interplanetary file system to support transparency, archiving, immutability, and secure peer-to-peer information sharing.The framework is also reported not to achieve optimum latency, limiting its IIoT use.
  • Communication Efficiency: FL communication overhead is a key concern because training recursively exchanges parameters between clients and servers in resource-constrained IIoT settings.
  • Secure Data Sharing: Federated Tensor Mining lets factories share ciphertext data for secure multisource tensor mining while preserving the knowledge obtained from plaintext mining.The approach is reported to defend against distributed eavesdroppers and centralized hackers.
  • Privacy-Preserving Monitoring: Communication-efficient FL anomaly detection combines decentralized training with an attention-based CNN-LSTM model for IIoT sensing.A top-k gradient-compression technique is used to reduce network overhead.

L. Data Integrity

FL-based IIoT systems pursue data integrity and privacy while addressing attacks on local data, model updates, and centralized models. The survey reviews blockchain and defensive-learning approaches, alongside data-management challenges and future privacy constraints.

  • Blockchain-based integrity: Blockchain-based FL can provide data integrity, but IIoT data heterogeneity and blockchain reliability remain design challenges.Existing security mechanisms are described as insufficient for an effective, reliable framework.
  • Blockchain-based integrity: A blockchain FL architecture detects IIoT failures by recording client-data Merkle-tree roots and measuring each client’s training impact.Each client server continuously builds a Merkle tree, while an on-chain system evaluates contribution using local training-data size.
  • Attack surfaces: FL remains vulnerable to data and model attacks spanning end devices, local models, global models, and centralized servers.Data attacks alter training data or labels, while model attacks modify updates or target training for misclassification.
  • Defensive learning: A federated defensive system combines a revised FL model with adversarial learning loss to address threats while preserving privacy across IIoT devices.Its results were compared with two popular benchmarks, although the supplied passage does not report the comparison outcome.
  • Survey scope: The survey consolidates privacy-preserving learning, blockchain, heterogeneous-data handling, and resource-management approaches for FL-enabled IIoT.It also reviews healthcare and automobile applications and identifies challenges, solutions, and future research directions.
  • Future directions: Future work should impose privacy constraints at both edge-device and data levels while supporting geographically distributed training without exposing sensitive information.The paper also identifies short-packet, low-latency communication limitations and points to 6G integration as a possible direction.
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