Device Scheduling

Device scheduling in distributed machine learning, particularly federated learning (FL), aims to optimize the selection and assignment of participating devices to minimize training time, energy consumption, and communication overhead while maximizing model accuracy. Current research focuses on developing efficient algorithms, often employing techniques like Lyapunov optimization, multi-armed bandits, reinforcement learning, and matching pursuit, to address challenges posed by heterogeneous devices, network constraints, and non-i.i.d. data. These advancements are crucial for enabling scalable and efficient FL deployments in resource-constrained environments like the Internet of Things (IoT), improving the practicality and performance of distributed AI applications. Furthermore, research explores novel frameworks like partial model aggregation to further enhance efficiency and address data heterogeneity.

Papers

August 26, 2024

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang, Yiyang Ni, Kang Wei, Wen Chen, Shi Jin
Digital Twin Internet of Thing Network Asynchronous Federated Learning Device Scheduling Device Selection

February 4, 2024

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things
Tinghao Zhang, Kwok-Yan Lam, Jun Zhao
Internet of Thing Internet of Thing Device Good Thing Hierarchical Federated Learning Edge Server Task Assignment Device Scheduling

February 1, 2024

Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
Muhammad Faraz Ul Abrar, Nicolò Michelusi
Communication Efficient Analog Neural Network Device Scheduling

December 15, 2023

Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning
Fan Zhang, Jining Chen, Kunlun Wang, Wen Chen
Federated Learning Power Allocation Air Aggregation Device Scheduling

May 26, 2023

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
Yuchang Sun, Zehong lin, Yuyi Mao, Shi Jin, Jun Zhang
Air Federated Learning Air Computation Channel Quality Uplink Control Device Scheduling

July 27, 2022

On-Device CPU Scheduling for Sense-React Systems
Aditi Partap, Samuel Grayson, Muhammad Huzaifa, Sarita Adve, Brighten Godfrey, Saurabh Gupta, Kris Hauser, Radhika Mittal
Real Time Augmented Reality Task Scheduling Robotic Framework Device Scheduling

June 14, 2022

Matching Pursuit Based Scheduling for Over-the-Air Federated Learning
Ali Bereyhi, Adela Vagollari, Saba Asaad, Ralf R. Müller, Wolfgang Gerstacker, H. Vincent Poor
Convex Relaxation Air Federated Learning Near Optimal Known Algorithm Aware Job Device Scheduling

April 20, 2022

Efficient Wireless Federated Learning with Partial Model Aggregation
Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan, Geoffrey Ye Li
Federated Learning Loss Function Model Aggregation Non Convex Loss Function Device Scheduling

January 19, 2022

Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Jake Perazzone, Shiqiang Wang, Mingyue Ji, Kevin Chan
Stochastic Optimization Convergence Analysis Wireless Network Non Convex Loss Function Power Allocation Device Scheduling

December 11, 2021

Efficient Device Scheduling with Multi-Job Federated Learning
Chendi Zhou, Ji Liu, Juncheng Jia, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou
Decentralized Data Parallel Training Federated Multi Task Learning Device Scheduling

Device Scheduling

Papers

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach

Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

On-Device CPU Scheduling for Sense-React Systems

Matching Pursuit Based Scheduling for Over-the-Air Federated Learning

Efficient Wireless Federated Learning with Partial Model Aggregation

Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

Efficient Device Scheduling with Multi-Job Federated Learning