mlsanityData to 14 Oct 2026

Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach

arXiv 2405.12046 ↗First posted 20 May 2024cs.LG cs.DC cs.ITcited by 6

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Abstract (arXiv)

Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without disclosing user-sensitive data. Specifically, in federated edge learning (FEEL) systems, the time-varying nature of wireless channels introduces inevitable system dynamics in the communication process, thereby affecting training latency and energy consumption. In this work, we further consider a streaming data scenario where new training data samples are randomly generated over time at edge devices. Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. To achieve this, we formulate a stochastic network optimization problem and use the Lyapunov drift-plus-penalty framework to obtain a dynamic resource management design. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. We provide convergence analysis for the considered setting with heterogeneous data and time-varying objective functions, which supports the rationale behind our proposed scheduling design. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 20 May 2024.

Builds on

29 of 54 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  6. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  7. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  8. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  9. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  10. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  11. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  12. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
17 more
  1. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  2. 2022Federated Learning under Distributed Concept Driftcited by 36
  3. 2020Time-Varying Convex Optimization: Time-Structured Algorithms and Applicationscited by 31
  4. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  5. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  6. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  7. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  8. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  9. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  10. 2020Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscited by 7
  11. 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
  12. 2022A Contribution-based Device Selection Scheme in Federated Learningcited by 7
  13. 2021Optimization-Based GenQSGD for Federated Edge Learningcited by 3
  14. 2022Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-runningcited by 3
  15. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  16. 2021Simultaneous Wireless Information and Power Transfer for Federated Learningcited by 2
  17. 2021Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneitycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stabilitycited by 2
  2. 2025Time-Varying Optimization for Streaming Data Via Temporal Weightingcited by 2
  3. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  4. 2026Decentralized Time-Varying Optimization for Streaming Data via Temporal Weightingcited by 1
  5. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  6. 2026Distributed Optimization with Streaming Data: A Temporal Weighting Perspectivecited by 0