Table 1.
Comparative Summary of Related Work
| Work | Approach / Technique | Key Contributions | Limitations / Gaps |
|---|---|---|---|
| [11] Ghanavati et al., 2020 | Ant-mating optimization for fog task scheduling | Energy-aware heuristic improves fog-level efficiency | Limited adaptability; no learning; not suitable for dynamic workloads |
| [12] Su et al., 2021 | Secure federated learning for smart grid | Privacy-preserving edge–cloud FL; secure aggregation | Not designed for online task scheduling; lacks real-time decision-making |
| [13] Baghban et al., 2022 | Actor–critic RL in federated edge computing | Improved IoT service provisioning; distributed RL training | No multi-objective modeling; no local queue optimization |
| [14] Soula et al., 2022 | Machine learning + bioinspired scheduling | Intelligent task allocation at edge; hybrid heuristics | Scalability concerns; lacks global coordination |
| [15] Ramezani et al., 2023 | RL-based scheduling in edge–fog–cloud | Multi-objective load balancing with RL | No federated coordination; centralized RL limits scalability |
| [16] Kar et al., 2023 | Survey on ML + optimization for cloud–edge–fog | Comprehensive taxonomy of offloading strategies | Highlights need for FL, multi-objective coordination, and dynamic adaptivity |
| [17] Kim et al., 2023 | Federated reinforcement learning for dynamic scheduling | Collaborative policy learning; improved coordination | No coupling between global policy and local task reordering |
| [18] Wu et al., 2024 | FRL for vehicular edge computing | Privacy-preserving scheduling for large AI models | High communication cost; no local execution optimization |
| [19] Shen et al., 2025 | RL-based scheduling in end–edge–cloud | RL for heterogeneous resource environments | Centralized data dependence; limited SLA/tardiness modeling |
| [20] Shidik et al., 2025 | Unsupervised cluster Q-learning | Energy minimization in federated edge cloud | Weak SLA handling; limited multiobjective scope |
| [21] Lilhore et al., 2025 | Hybrid cloud–edge deep learning | Scalable resource optimization for IoT | Does not integrate global offloading + local queue optimization |
Figure 1.
System Architecture
Figure 2.
Workflow of the proposed work
Table 2.
Summary of Simulation Environment
| Category | Configuration Details |
|---|---|
| Task Characteristics | 50–200 heterogeneous tasks per run; varied CPU, memory, and latency requirements |
| Edge Nodes | 10 nodes with low capacity and limited energy |
| Fog Nodes | 5 intermediate nodes with moderate processing and energy |
| Cloud Data Centers | 3 high-performance data centers with abundant energy and low resource constraints |
| System Heterogeneity | Varying processing speed, power consumption, and network latency across nodes |
| Workload Types | Real-time loT streams, batch tasks, latency-sensitive applications |
Table 3.
Performance Metrics
| Metric | Description |
|---|---|
| Task Completion Time (TCT) | Average time required to complete all scheduled tasks. |
| Energy Consumption | Total energy utilized across edge, fog, and cloud layers. |
| Load Balance Rate (LBR) | Standard deviation of the task load distributed across all nodes. Lower is better. |
| Task Success Ratio (TSR) | Percentage of tasks completed successfully within their specified deadlines. |
| Scheduling Overhead | Time and resource consumption incurred by the scheduling mechanism itself. |
| Model Convergence | Convergence behavior and stability of federated reinforcement learning agents over training rounds. |
Figure 3.
Task completion time comparison
Figure 4.
Energy efficiency comparison
Figure 5.
Load balancing comparison
Figure 6.
Task success ratio comparison
Figure 7.
Scheduling overhead comparison
Figure 8.
Model convergence comparison
Table 4.
Ablation Study of System Components
| Configuration | Strengths | Limitations | Overall Outcome |
|---|---|---|---|
| FRL-Only |
|
|
|
| ABC-Only |
|
|
|
| Hybrid FRL + A-ABC (Proposed) |
|
|
|
Table 5.
Sensitivity Analysis of Key Hyperparameters
| Parameter Group | Hyperparameters Tested | Sensitivity Observation |
|---|---|---|
| FRL Module | Learning rate, discount factor, exploration rate | Minor performance variations; stable convergence within broad ranges |
| A-ABC Module | Colony size, limit value, employed/onlooker ratio | Larger colonies improve quality but increase overhead; performance remains stable across typical ranges |
| Federated Learning | Aggregation frequency, participation rate, non-IID severity | Hybrid model remains robust; slight slowdown under extreme non-IID settings but no convergence failures |