Blockchain-Based Decentralized Edge Intelligence Collaboration and Adaptive Incentive Mechanism Research
Year: 2025
Type: article
Abstract: Edge IIoT learning suffers from non-IID data, stragglers, and untrusted coordination that raise latency, energy, and variance. Our objective is to enable reliable, privacy-preserving, and low-latency collaborative learning for edge IIoT under these conditions. We present BEACON, which couples federated learning with a CTDE-MADDPG adaptive incentive on a permissioned blockchain, uses lightweight robust aggregation (FedProx/Krum/Trimmed-Mean), and anchors only hashes on-chain for audit while keeping model payloads off-chain. The system runs over heterogeneous LoRa/Wi-Fi links and supports optional differential-privacy noise and Byzantine-tolerant modes. On a Raspberry Pi testbed and 10–100-node simulations, BEACON improves task completion and accuracy while lowering latency and CPU/energy relative to a static-incentive approach; the blockchain overhead remains modest at a 1-s block time. We also report sensitivity to reward weights and non-IID levels and outline deployment limits with practical mitigations. Compared with a static-incentive baseline, BEACON increases task completion by 19.7% and throughput by 15.4%, while reducing latency by 24.8% and CPU usage by 22.6% under identical budgets; improvements are statistically significant.
Source: IEEE Internet of Things Journal
Authors: Ting Chai, Kunhee Han, Seungsoo Shin
Institutions: Tongmyong University, Baekseok University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W4414197226