A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
Not on the map. Why?
Abstract (arXiv)
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 67 of its 139 references that are in the map. First posted on arXiv 10 Oct 2024.
Builds on
67 of 139 references.
References · 67
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
- 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
- 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
- 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
- 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
- 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
- 2021Federated Learning on Non-IID Data: A Surveycited by 346
- 2017Sparse Communication for Distributed Gradient Descentcited by 249
38 more
- 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
- 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
- 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
- 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
- 2022Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingcited by 56
- 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
- 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
- 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
- 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
- 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
- 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
- 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
- 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
- 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
- 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 18
- 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
- 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
- 2024Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNetscited by 17
- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
- 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 13
- 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
- 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
- 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
- 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
- 2021Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Schedulingcited by 9
- 2024Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systemscited by 9
- 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
- 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
- 2021Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Schedulingcited by 8
- 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
- 2021Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networkscited by 7
- 2024DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computingcited by 7
- 2021Data-Aware Device Scheduling for Federated Edge Learningcited by 6
- 2022Data-Quality Based Scheduling for Federated Edge Learningcited by 6
- 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
- 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
50 most cited of 67 All 67 (JSON)
Built on by
Citing papers per year: 2025: 1.
1 paper.