# Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning

arXiv: https://arxiv.org/abs/2307.00541 · HTML: https://mlsanity.com/paper/2307.00541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring](https://mlsanity.com/paper/2012.07450/) (2020)
- [Multi-Armed Bandit Based Client Scheduling for Federated Learning](https://mlsanity.com/paper/2007.02315/) (2020)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints](https://mlsanity.com/paper/2205.06730/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning](https://mlsanity.com/paper/2103.01422/) (2021)
- [FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT](https://mlsanity.com/paper/2012.06150/) (2020)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)
- [Partially Observable Markov Decision Process for Recommender Systems](https://mlsanity.com/paper/1608.07793/) (2016)
- [DER Forecast using Privacy Preserving Federated Learning](https://mlsanity.com/paper/2107.03248/) (2021)
- [Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework](https://mlsanity.com/paper/1802.06402/) (2018)
- [Low-Complexity Dynamic Resource Scheduling for Downlink MC-NOMA Over Fading Channels](https://mlsanity.com/paper/2103.10650/) (2021)

## Built on by (most cited first, 3 of 3)

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears](https://mlsanity.com/paper/2508.17262/) (2025)

## Abstract (arXiv)

In this paper, we examine cloud-edge-terminal IoT networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for them to learn their own policy from scratch. Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks. To this end, we propose a novel collaborative policy learning framework for dynamic scheduling tasks using federated reinforcement learning. For effective learning, our framework adaptively selects the tasks for collaborative learning in each round, taking into account the need for fairness among tasks. In addition, as a key enabler of the framework, we propose an edge-agnostic policy structure that enables the aggregation of local policies from different edges. We then provide the convergence analysis of the framework. Through simulations, we demonstrate that our proposed framework significantly outperforms the approaches without collaborative policy learning. Notably, it accelerates the learning speed of the policies and allows newly arrived edges to adapt to their tasks more easily.