Federated Learning over Energy Harvesting Wireless Networks
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Abstract (arXiv)
In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) employs massive multiple-input multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to the interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the factors such as transmit power and number of scheduled users affect the training loss, the convergence rate of the FL algorithm is first analyzed. Given this analytical result, the user scheduling and energy management optimization problem can be decomposed, simplified, and solved. Further, the system model is extended by considering multiple BSs. Hence, a joint user association and scheduling problem in FL over wireless systems is studied. The optimal user association problem is solved using the branch-and-bound technique. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2021.
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Citing papers per year: 2022: 6; 2023: 4; 2024: 3; 2025: 3; 2026: 1.
17 papers.
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- 2023Green Edge AI: A Contemporary Surveycited by 24
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
- 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
- 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
- 2022On Federated Learning with Energy Harvesting Clientscited by 4
- 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
- 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
- 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHAcited by 2
- 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
- 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
5 more
- 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updatescited by 0
- 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Clientcited by 0
- 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
- 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0
- 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization2 Aug 2026
- 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling14 Nov 2025
- 2025Federated Learning-Distillation Alternation for Resource-Constrained IoT26 May 2025
- 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning16 Apr 2025
- 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach21 Jun 2024
- 2024Federated Learning With Energy Harvesting Devices: An MDP Framework17 May 2024
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client13 May 2024
- 2023Green Edge AI: A Contemporary Survey1 Dec 2023
- 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA12 Sep 2023
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference10 May 2023
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks18 Apr 2023
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles27 Oct 2022
5 more
- 2022Random Orthogonalization for Federated Learning in Massive MIMO Systems18 Oct 2022
- 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach14 Sep 2022
- 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations25 Aug 2022
- 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates3 May 2022
- 2022On Federated Learning with Energy Harvesting Clients12 Feb 2022
- 2023Green Edge AI: A Contemporary Surveycited by 24
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
- 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
- 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
- 2022On Federated Learning with Energy Harvesting Clientscited by 4
- 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
- 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
- 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHAcited by 2
- 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
- 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
12 most cited of 17 All 17 (JSON)
- 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization2 Aug 2026
- 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling14 Nov 2025
- 2025Federated Learning-Distillation Alternation for Resource-Constrained IoT26 May 2025
- 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning16 Apr 2025
- 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach21 Jun 2024
- 2024Federated Learning With Energy Harvesting Devices: An MDP Framework17 May 2024
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client13 May 2024
- 2023Green Edge AI: A Contemporary Survey1 Dec 2023
- 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA12 Sep 2023
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference10 May 2023
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks18 Apr 2023
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles27 Oct 2022
12 newest of 17 All 17 (JSON)