Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT
The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. This paper introduces the Hierarchical Multi-Agent Federated Actor-Critic (HMAFAC) algorithm. This nov...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10965621/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850177441737211904 |
|---|---|
| author | S. Benila K. Devi |
| author_facet | S. Benila K. Devi |
| author_sort | S. Benila |
| collection | DOAJ |
| description | The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. This paper introduces the Hierarchical Multi-Agent Federated Actor-Critic (HMAFAC) algorithm. This novel approach integrates hierarchical reinforcement learning with multi-agent federated learning to enhance decision-making in dynamic edge computing environments. It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. A Deep Q-Network (DQN) agent manages task offloading decisions based on real-time system states, ensuring efficient task execution. To maintain data privacy and reduce communication overhead, federated learning aggregates model updates at the edge server, eliminating the need for raw data exchange. Additionally, a hierarchical framework enables adaptive scheduling, allowing flexible computational scaling from edge to cloud servers based on task complexity and network conditions. By effectively reducing latency and energy consumption, enhancing system adaptability, and maintaining computational efficiency, HMAFAC overcomes key limitations of existing IIoT frameworks. Simulation results validate its superior performance compared to baseline methods regarding resource utilization, energy conservation, and model convergence. These findings establish HMAFAC as a robust and scalable framework for optimizing IIoT applications in distributed and resource-constrained environments. Future work will further explore advanced federated learning techniques, enhanced privacy mechanisms, and real-world deployment in industrial systems to improve efficiency and scalability. |
| format | Article |
| id | doaj-art-d0b95cee93754a14baef97b438780419 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0b95cee93754a14baef97b4387804192025-08-20T02:18:58ZengIEEEIEEE Access2169-35362025-01-0113683116832210.1109/ACCESS.2025.356078110965621Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoTS. Benila0https://orcid.org/0000-0003-3443-4475K. Devi1https://orcid.org/0000-0002-8824-5285School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu, IndiaThe Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. This paper introduces the Hierarchical Multi-Agent Federated Actor-Critic (HMAFAC) algorithm. This novel approach integrates hierarchical reinforcement learning with multi-agent federated learning to enhance decision-making in dynamic edge computing environments. It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. A Deep Q-Network (DQN) agent manages task offloading decisions based on real-time system states, ensuring efficient task execution. To maintain data privacy and reduce communication overhead, federated learning aggregates model updates at the edge server, eliminating the need for raw data exchange. Additionally, a hierarchical framework enables adaptive scheduling, allowing flexible computational scaling from edge to cloud servers based on task complexity and network conditions. By effectively reducing latency and energy consumption, enhancing system adaptability, and maintaining computational efficiency, HMAFAC overcomes key limitations of existing IIoT frameworks. Simulation results validate its superior performance compared to baseline methods regarding resource utilization, energy conservation, and model convergence. These findings establish HMAFAC as a robust and scalable framework for optimizing IIoT applications in distributed and resource-constrained environments. Future work will further explore advanced federated learning techniques, enhanced privacy mechanisms, and real-world deployment in industrial systems to improve efficiency and scalability.https://ieeexplore.ieee.org/document/10965621/Deep Q-Networkedge computingfederated learninghierarchical multi-agent federated actor-critic (HMAFAC)Industrial Internet of Thingstask offloading |
| spellingShingle | S. Benila K. Devi Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT IEEE Access Deep Q-Network edge computing federated learning hierarchical multi-agent federated actor-critic (HMAFAC) Industrial Internet of Things task offloading |
| title | Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT |
| title_full | Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT |
| title_fullStr | Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT |
| title_full_unstemmed | Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT |
| title_short | Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT |
| title_sort | federated synergy hierarchical multi agent learning for sustainable edge computing in iiot |
| topic | Deep Q-Network edge computing federated learning hierarchical multi-agent federated actor-critic (HMAFAC) Industrial Internet of Things task offloading |
| url | https://ieeexplore.ieee.org/document/10965621/ |
| work_keys_str_mv | AT sbenila federatedsynergyhierarchicalmultiagentlearningforsustainableedgecomputinginiiot AT kdevi federatedsynergyhierarchicalmultiagentlearningforsustainableedgecomputinginiiot |