# Secure Resource Allocation via Constrained Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2501.11557 · HTML: https://mlsanity.com/paper/2501.11557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 28 of 28)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2404.06448/) (2024)
- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2403.16460/) (2024)
- [LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks](https://mlsanity.com/paper/2501.01293/) (2025)
- [IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers](https://mlsanity.com/paper/2410.02592/) (2024)
- [Backdooring Neural Code Search](https://mlsanity.com/paper/2305.17506/) (2023)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2501.01850/) (2025)
- [Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples](https://mlsanity.com/paper/2501.02775/) (2025)
- [Adaptive Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2312.01081/) (2023)
- [Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective](https://mlsanity.com/paper/2501.03562/) (2025)
- [Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders](https://mlsanity.com/paper/2406.03508/) (2024)
- [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024)
- [Semantic Sleuth: Identifying Ponzi Contracts via Large Language Models](https://mlsanity.com/paper/2411.07498/) (2024)
- [A Survey of Source Code Search: A 3-Dimensional Perspective](https://mlsanity.com/paper/2311.07107/) (2023)
- [Can Distillation Mitigate Backdoor Attacks in Pre-trained Encoders?](https://mlsanity.com/paper/2403.03846/) (2024)
- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing](https://mlsanity.com/paper/2408.10116/) (2024)
- [Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey](https://mlsanity.com/paper/2406.08115/) (2024)
- [Security modeling and efficient computation offloading for service workflow in mobile edge computing](https://mlsanity.com/paper/1907.02506/) (2019)
- [Architecture and Performance Evaluation of Distributed Computation Offloading in Edge Computing](https://mlsanity.com/paper/2109.09415/) (2021)
- [Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems](https://mlsanity.com/paper/2405.17100/) (2024)

## Built on by (most cited first, 1 of 1)

- [Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments](https://mlsanity.com/paper/2512.22149/) (2025)

## Abstract (arXiv)

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios, including different task loads, data sizes, and MEC capacities, show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.