Proceedings Article10.1109/ICC45855.2022.9882281
Federated Learning for Distributed Energy-Efficient Resource Allocation
Zelin Ji,Zhijin Qin +1 more
- 20 Apr 2022
pp 1-6
6
TL;DR: Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
read more
Abstract: In cellular networks, resource allocation is performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper investigates the distributed resource allocation scheme for cellular networks to maximize the energy efficiency of the system in the uplink transmission, while guaranteeing the quality of service (QoS) for cellular users. Particularly, to cope the fast varying channels in wireless communication environment, we propose a robust federated reinforcement learning (FRL_suc) framework to enable local users perform distributed resource allocation in items of transmit power and channel assignment by the guidance of the the local neural network trained at each user. Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Multi-user Deep Semantic Communication System based on Federated Learning with Dynamic Model Aggregation
Huanlai Xing,Haolan Zhang,Xinhan Wang,Lexi Xu,Zhiwen Xiao,Shouxi Luo,Li Feng,Yuanshun Dai +7 more
- 28 May 2023
TL;DR: Simulation results demonstrate that the proposed DeepSC-FedDMA overwhelms the semantic communication systems with federated averaging and pure local training in terms of mean square error and peak signal-to-noise ratio, under three kinds of channels.
6
A Comprehensive Survey on Energy Efficiency in Federated Learning: Strategies and Challenges
Ala Gouissem,Zina Chkirbene,Ridha Hamila +2 more
- 04 Mar 2024
TL;DR: A comprehensive survey of current methods and techniques aimed at enhancing energy efficiency in FL systems, delve into various resource allocation techniques and algorithm optimization strategies, and examines the role of cutting-edge technologies such as Blockchain and 6G networks.
3
Meta Federated Reinforcement Learning for Distributed Resource Allocation
Zelin Ji,Zhijin Qin,Xiaoming Tao +2 more
TL;DR: In this article , a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information.
3
Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research Trends
Sree Krishna Das,Ratna Mudi,Md. Siddikur Rahman,Khaled M. Rabie,Xingwang Li +4 more
TL;DR: This paper explores federated reinforcement learning (FRL) for wireless networks, addressing challenges in 5G networks, such as latency and energy consumption, by implementing distributed learning techniques, including FRL, MARL, and the FRL framework, to improve learning efficiency and network robustness.
3
Federated-Multi Agent DDQN-Empowered Joint Optimization of Mode Selection and Resource Allocation in D2D-Assisted 6G Wireless Networks
Hafiz Muhammad Fahad Noman,Kaharudin Dimyati,Effariza Hanafi,Kamarul Ariffin Noordin,Atef Abdrabou +4 more
TL;DR: This paper proposes a federated multiagent DDQN framework (F-MADDQN) for joint mode selection and resource allocation in D2D-assisted 6G networks, achieving 17.55-110.26% higher energy efficiency and 21.70-55.43% lower outage probabilities compared to existing methods.
References
•Book
Reinforcement Learning: An Introduction
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
•Posted Content
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Alex Graves,Ioannis Antonoglou,Daan Wierstra,Martin Riedmiller +6 more
TL;DR: This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
•Posted Content
Towards Federated Learning at Scale: System Design
Keith Bonawitz,Hubert Eichner,Wolfgang Grieskamp,Dzmitry Huba,Alex Ingerman,Vladimir Ivanov,Chloe Kiddon,Jakub Konečný,Stefano Mazzocchi,H. Brendan McMahan,Timon Van Overveldt,David Petrou,Daniel Ramage,Jason Roselander +13 more
TL;DR: In this paper, a scalable production system for federated learning in the domain of mobile devices, based on TensorFlow, is presented. Butler et al. describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.
•Posted Content
Federated Learning for Mobile Keyboard Prediction
Andrew Straiton Hard,Chloe Kiddon,Daniel Ramage,Francoise Beaufays,Hubert Eichner,Kanishka Rao,Rajiv Mathews,Sean Augenstein +7 more
TL;DR: The federation algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall and the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers are demonstrated.
1.6K