FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning

• URL: http://arxiv.org/abs/2511.15454v1
• Date: Wed, 19 Nov 2025 14:11:44 GMT
• Title: FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
• Authors: Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou,
• Abstract summary: We propose FairEnergy to balance energy efficiency and fair participation in wireless edge systems.<n>We show that FairEnergy achieves higher accuracy while reducing by up to 79% compared to baseline strategies.
• Score: 8.462545504525805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, balancing energy efficiency and fair participation while ensuring high model accuracy remains challenging in wireless edge systems due to heterogeneous resources, unequal client contributions, and limited communication capacity. To address these challenges, we propose FairEnergy, a fairness-aware energy minimization framework that integrates a contribution score capturing both the magnitude of updates and their compression ratio into the joint optimization of device selection, bandwidth allocation, and compression level. The resulting mixed-integer non-convex problem is solved by relaxing binary selection variables and applying Lagrangian decomposition to handle global bandwidth coupling, followed by per-device subproblem optimization. Experiments on non-IID data show that FairEnergy achieves higher accuracy while reducing energy consumption by up to 79\% compared to baseline strategies.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation [5.593872199021558]
  We propose FedBacys, a battery-aware EHFL framework using cyclic client participation based on users' battery levels.<n>By clustering clients and scheduling them sequentially, FedBacys minimizes redundant computations, reduces system-wide energy usage, and improves learning stability.<n>We also introduce FedBacys-Odd, a more energy-efficient variant that allows clients to participate selectively, further reducing energy costs without compromising performance.
  arXiv  Detail & Related papers  (2025-11-14T23:46:48Z)
• Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design [43.89869891417806]
  Real-time computer vision (CV) applications on wireless edge devices demand energy-efficient and privacy-preserving learning.<n>We propose FedDPQ, an ultra energy-efficient FL framework for real-time CV over unreliable wireless networks.
  arXiv  Detail & Related papers  (2025-08-03T13:05:11Z)
• BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT [2.6872737601772956]
  In Mobile Edge IoT (MEIoT), the training and communication processes can significantly deplete the limited battery resources of devices.<n>We propose BEFL, a joint optimization framework aimed at balancing three objectives: enhancing global model accuracy, minimizing total energy consumption, and reducing energy usage disparities among devices.<n>Our experiments reveal that BEFL improves global model accuracy by 1.6%, reduces energy consumption variance by 72.7%, and lowers total energy consumption by 28.2% compared to existing methods.
  arXiv  Detail & Related papers  (2024-12-05T07:58:32Z)
• Learn More by Using Less: Distributed Learning with Energy-Constrained Devices [4.036740581753959]
  Federated Learning (FL) has emerged as a solution for distributed model training across decentralized, privacy-preserving devices.<n>We propose LeanFed, an energy-aware FL framework designed to optimize client selection and training workloads on battery-constrained devices.
  arXiv  Detail & Related papers  (2024-12-03T09:06:57Z)
• Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning [40.96977338485749]
  This paper presents a novel approach that ensures energy efficiency for distributionally robust federated learning (FL) with over air computation (AirComp) We introduce a novel client selection method that integrates two complementary insights: a deterministic one that is designed for energy efficiency, and a probabilistic one designed for distributional robustness. Simulation results underscore the efficacy of the proposed algorithm, revealing its superior performance compared to baselines from both robustness and energy efficiency perspectives.
  arXiv  Detail & Related papers  (2023-12-22T12:15:52Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.