# Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT

arXiv: https://arxiv.org/abs/2202.07391 · HTML: https://mlsanity.com/paper/2202.07391/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Multi-Armed Bandit Based Client Scheduling for Federated Learning](https://mlsanity.com/paper/2007.02315/) (2020)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Learning Private Neural Language Modeling with Attentive Aggregation](https://mlsanity.com/paper/1812.07108/) (2018)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks](https://mlsanity.com/paper/2009.13879/) (2020)
- [Security in Mobile Edge Caching with Reinforcement Learning](https://mlsanity.com/paper/1801.05915/) (2018)
- [Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1812.03633/) (2018)
- [Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing](https://mlsanity.com/paper/2012.14137/) (2020)
- [Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling](https://mlsanity.com/paper/2106.06934/) (2021)
- [Federated Learning for Coalition Operations](https://mlsanity.com/paper/1910.06799/) (2019)

## Built on by (most cited first, 12 of 12)

- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey](https://mlsanity.com/paper/2312.08667/) (2023)
- [Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach](https://mlsanity.com/paper/2311.18498/) (2023)
- [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023)
- [Leverage Variational Graph Representation For Model Poisoning on Federated Learning](https://mlsanity.com/paper/2404.15042/) (2024)
- [Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review](https://mlsanity.com/paper/2407.18096/) (2024)
- [Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning](https://mlsanity.com/paper/2505.11570/) (2025)
- [A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT](https://mlsanity.com/paper/2410.01644/) (2024)
- [Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion](https://mlsanity.com/paper/2207.11447/) (2022)
- [The Two Faces of AI in Green Mobile Computing: A Literature Review](https://mlsanity.com/paper/2308.04436/) (2023)
- [A Framework for testing Federated Learning algorithms using an edge-like environment](https://mlsanity.com/paper/2407.12980/) (2024)
- [Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders](https://mlsanity.com/paper/2504.10067/) (2025)

## Abstract (arXiv)

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large datasets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small datasets for FL, resulting in a falling learning accuracy. In this paper, we formulate a new resource allocation problem for privacy-persevering EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new federated learning-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to existing state-of-the-art benchmark.