Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Marnissi, Ouiame, Hammouti, Hajar EL, Bergou, El Houcine
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Joint Probability Selection and Power Allocation for Federated Learning
by: Marnissi, Ouiame, et al.
Published: (2024)

Semantic-Aware Resource Allocation in Constrained Networks with Limited User Participation
by: Marnissi, Ouiame, et al.
Published: (2024)

Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design
by: Hammouti, Hajar El, et al.
Published: (2024)

Just Few States are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems
by: Hadach, Zaid, et al.
Published: (2025)

If You Want to Be Robust, Be Wary of Initialization
by: Ennadir, Sofiane, et al.
Published: (2025)

Stabilizing Policy Gradient Methods via Reward Profiling
by: Ahmed, Shihab, et al.
Published: (2025)

FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
by: Wen, Siyuan, et al.
Published: (2025)

Fairness in Federated Learning: Fairness for Whom?
by: Taik, Afaf, et al.
Published: (2025)

Fairness Regularization in Federated Learning
by: Kharaghani, Zahra, et al.
Published: (2025)

Energy-Tweedie: Score meets Score, Energy meets Energy
by: Leban, Andrej
Published: (2025)

Federated Learning with Relative Fairness
by: Nakakita, Shogo, et al.
Published: (2024)

Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
by: Duan, Yuying, et al.
Published: (2024)

WassFFed: Wasserstein Fair Federated Learning
by: Han, Zhongxuan, et al.
Published: (2024)

Entropy-driven Fair and Effective Federated Learning
by: Wang, Lin, et al.
Published: (2023)

Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
by: Zhang, Meiying, et al.
Published: (2024)

AFed: Algorithmic Fair Federated Learning
by: Chen, Huiqiang, et al.
Published: (2025)

pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
by: Lei, Haoyu, et al.
Published: (2025)

Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
by: Mukhtiar, Noorain, et al.
Published: (2026)

Loss Gap Parity for Fairness in Heterogeneous Federated Learning
by: Erraji, Brahim, et al.
Published: (2026)

FairGLVQ: Fairness in Partition-Based Classification
by: Störck, Felix, et al.
Published: (2024)

Fairness in Federated Learning: Trends, Challenges, and Opportunities
by: Mukhtiar, Noorain, et al.
Published: (2025)

Fairness-Constrained Optimization Attack in Federated Learning
by: Kasyap, Harsh, et al.
Published: (2025)

Federated Fairness Analytics: Quantifying Fairness in Federated Learning
by: Dilley, Oscar, et al.
Published: (2024)

Semi-Variance Reduction for Fair Federated Learning
by: Malekmohammadi, Saber, et al.
Published: (2024)

LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
by: Zhang, Li, et al.
Published: (2025)

FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
by: Zhou, Zihao, et al.
Published: (2025)

From Efficiency to Equity: Measuring Fairness in Preference Learning
by: Gowaikar, Shreeyash, et al.
Published: (2024)

A Post-Processing-Based Fair Federated Learning Framework
by: Zhou, Yi, et al.
Published: (2025)

Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
by: Bian, Zeyu, et al.
Published: (2026)

Distributed Quasi-Newton Method for Fair and Fast Federated Learning
by: Hamidi, Shayan Mohajer, et al.
Published: (2025)

PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning
by: Gao, Jiashi, et al.
Published: (2024)

FedFair^3: Unlocking Threefold Fairness in Federated Learning
by: Javaherian, Simin, et al.
Published: (2024)

BoostFGL: Boosting Fairness in Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)

Fair Concurrent Training of Multiple Models in Federated Learning
by: Siew, Marie, et al.
Published: (2024)

Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
by: Zhu, Shengkun, et al.
Published: (2024)

Boosting Fairness and Robustness in Over-the-Air Federated Learning
by: Oksuz, Halil Yigit, et al.
Published: (2024)

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
by: Ye, Rongguang, et al.
Published: (2025)

The Cost of Local and Global Fairness in Federated Learning
by: Duan, Yuying, et al.
Published: (2025)

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
by: Heilmann, Xenia, et al.
Published: (2025)