Dynamic Scheduling for Federated Edge Learning with Streaming Data
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Abstract (arXiv)
In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 2 May 2023.
Builds on
16 of 27 references.
References · 16
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
- 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
- 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
- 2020Federated Learning under Importance Samplingcited by 26
- 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
- 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
- 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
- 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
4 more
- 2021Federated Learning at the Network Edge: When Not All Nodes are Created Equalcited by 10
- 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
- 2021Optimization-Based GenQSGD for Federated Edge Learningcited by 3
- 2022Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspectivecited by 1
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.