Research Article
Collaborative Caching in Edge Computing via Federated Learning and Deep Reinforcement Learning
Table 1
Comparison of existing papers addressing edge caching problems.
| Reference | Optimization objective | Method | Disadvantages |
| [22] | Download latency | Hungarian algorithm | No quantitative benefits | [23] | Cache hit ratio | Greedy algorithm | High complexity | [24] | Cache hit ratio | Bidirectional recurrent neural networks | Privacy security | [25] | Energy consumption | Branch and bound algorithm | Privacy security | [26] | Estimating content popularity | Federated -means scheme | High complexity | [27] | Minimize traffic cost | Federated learning | Lower model accuracy | [29] | User response latency | Heuristic algorithm | Homogeneous user demand distribution | [30] | The cost of the video provider | Branch and bound algorithm | High time consuming |
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