Table 1 Summary of Blockchain-based federated learning (BCFL) Literature.
Authors | Application/Focus | Problem Addressed | Key Contribution | Limitations |
|---|---|---|---|---|
Chang et al. (2021) | Smart Healthcare | Privacy and model integrity | Secure FL using blockchain; improved accuracy | High computational/communication overhead; scalability concerns |
Ren et al. (2024) | Edge Computing | Model aggregation efficiency | Scalable blockchain-enabled FL for edge nodes | Trade-off between security and efficiency; high energy consumption |
Cao et al. (2023) | On-device FL | Decentralization without central authorities | DAG-based blockchain FL with improved transparency | Blockchain consistency, storage overhead |
Mahato et al. (2024) | Privacy-preserving FL | Adversarial protection | Homomorphic encryption integration | High computational cost, latency |
Zhang et al. (2023) | Fairness in FL | Bias in federated models | ZKP-enhanced fairness and verification | High computational cost, communication bottlenecks |
Jiang et al. (2021) | Secure participation in FL | Unauthorized participation | Membership proof techniques for secure device authentication | Computational overhead, sybil attacks not addressed |
Alqahtani et al. (2024) | IoT Networks | Secure transmission | Homomorphic encryption with optical fiber | High encryption complexity, poor key management |
Wang et al. (2024) | Healthcare FL | Incremental data integration | Blockchain-enabled data sharing | Storage redundancy, lack of incentive mechanism |
Jia et al. (2024) | Multi-task FL | Simultaneous training of multiple tasks | Blockchain-supported concurrent model training | Performance inconsistency due to data heterogeneity |
Guduri et al. (2023) | EHR security | Cross-hospital FL privacy | Blockchain-secured EHR sharing | Inefficient for real-time applications due to overhead |
Badr et al. (2023) | Smart Grids | Energy forecasting privacy | FL-enabled prediction with privacy | High communication cost, transaction delays |
Abdulla et al. (2024) | Smart Cities | Energy consumption prediction | Adaptive FL for energy demand | Inefficient under dynamic fluctuations, storage issues |
Joyce et al. (2024) | Smart City Regulations | Regulatory compliance | Analysis of data sharing vs. protection | Lacks implementation framework |
Li et al. (2023) | IoV (Internet of Vehicles) | Task allocation and privacy | Matching mechanism for FL tasks | Vulnerable to adversarial attacks, no incentive mechanism |
Zhao et al. (2023) | Energy-Efficient FL | Energy-performance trade-off | Stackelberg game-based optimization | High computational complexity |
Wu et al. (2023) | Lightweight FL | Security vulnerabilities | Threat analysis of FL models | No mitigation strategies proposed |
Hu et al. (2023) | Industrial IoT | Privacy-preserving FL in hydrocarbon exploration | FL model for IIoT privacy | No real-world deployment results |
