Adaptive Layer-Wise Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Caching
Bibliographic record
Abstract
Proactive caching is essential for minimizing latency and improving Quality of Experience (QoE) in heterogeneous edge networks. While Federated Deep Reinforcement Learning (FDRL) shows promise for developing cache policies, it faces challenges such as an expanding action space and difficulty in balancing global knowledge sharing with local environmental adaptation. In this paper, we propose a Layer-wise Relevance Propagation-aided Personalized Federated (LRP-PFed) Deep Reinforcement Learning framework for edge caching to maximize system utility while satisfying caching constraints. To handle the expanding action space, we design a Multi-Head Double Deep Q-Network (MH-DDQN) that reshapes the action output layers into a multi-head structure, where each head generates a sub-dimensional action. Furthermore, we introduce an LRP-based adaptive personalization mechanism that dynamically determines the optimal number of personalized layers for each edge server during training. This approach enables automatic adaptation to heterogeneous environments while leveraging global information to accelerate learning convergence. Extensive experiments validate the effectiveness of our approach, showing that MH-DDQN achieves superior cache hit rates and reduced computational complexity compared to traditional DRL methods, while our LRP-guided personalization strategy achieves superior performance, scalability, and adaptivity compared to existing FDRL methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".