FEDORA: Federated Ensemble Reinforcement Learning for DAG-Based Task Offloading and Resource Allocation in MEC
Bibliographic record
Abstract
The increasing demand for compute intensive Internet of Thing (IoT) applications has accelerated the adoption of multi-access edge Computing (MEC) to offload tasks from resource constrained devices to edge servers. However, making optimal offloading decisions in multi-user MEC environments is challenging due to the dependencies between tasks, resource constraints, and the need to preserve user privacy. In this work, we propose FEDORA, a federated ensemble reinforcement learning framework for directed acyclic graph (DAG)-based task Offloading and resource allocation in MEC environments, that integrates twin delayed deep deterministic policy gradient (TD3) for continuous resource allocation and multi-head deep Q-networks (DQN) for discrete offloading decisions. To handle task dependencies, we model applications as DAGs and generate feature embeddings for offloading decisions. Our federated learning (FL) approach uses local training at MEC level and periodic model aggregation at a global server to preserve data privacy. Finally, extensive simulations across different DAG topologies demonstrate that FEDORA reduces system costs and improves task completion rates compared to state-of-the-art baselines including FL-DQN, FL-DDPG, FedAvg, FedNova, and SCAFFOLD, highlighting its scalability and robustness in large scale MEC deployments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".