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

by   Do-Yup Kim, et al.

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.


Scheduling in Cellular Federated Edge Learning with Importance and Channel Awareness

In cellular federated edge learning (FEEL), multiple edge devices holdin...

System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy

Dynamic scheduling is an important problem in applications from queuing ...

Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

Personalized Federated Learning (PFL) is a new Federated Learning (FL) p...

E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI

Traditionally, AI models are trained on the central cloud with data coll...

OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints

Distributed machine learning (ML) at network edge is a promising paradig...

Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

Kubernetes (k8s) has the potential to coordinate distributed edge resour...

FLAP – A Federated Learning Framework for Attribute-based Access Control Policies

Technology advances in areas such as sensors, IoT, and robotics, enable ...

Please sign up or login with your details

Forgot password? Click here to reset