Fairness-Aware Learning

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Period ending 2026-09-21

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Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
May 12, 2026Satwat Bashir, Tasos Dagiuklas, Muddesar IqbalHeterogeneous Federated Learning Federated Learning
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
May 12, 2026Alberto Fernández-Hernández, Jose I. Mestre, Cristian Pérez-Corral +3Federated Learning Fedavg
DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation
May 11, 2026Yiqi Tian, Sangjoon Park, Bo Zeng +3Fairness-Aware Learning Medical Image Segmentation
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
May 9, 2026Bingnan Xiao, Yuan Gao, Bingcong Li +3Federated Learning Sharpness-Aware Minimization
Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
May 9, 2026Aishwarya Fursule, Shruti Kshirsagar, Anderson R. AvilaAudio Deepfake Detection Acoustic Representation
When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
May 9, 2026Kiran Naseer, Umar ShoaibFederated Learning Fairness-Aware Learning
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
May 9, 2026Chia-Yuan Wu, Frank E. Curtis, Daniel P. RobinsonFairness-Aware Learning Unreliable Reasoning
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
May 8, 2026Fabian Stricker, Jose A. Peregrina, David Bermbach +1Federated Learning Model Evaluation
Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
May 8, 2026Ozgu Goksu, Nicolas PugeaultFederated Learning Distributional Learning
Quotient Semivalues for False-Name-Resistant Data Attribution
May 8, 2026Florian A. D. Burnat, Brittany I. DavidsonShapley Value Fairness-Aware Learning
Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
May 7, 2026John Wang, Mengfan XuFairness-Aware Learning Game Theory
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
May 6, 2026Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2Federated Learning Fairness-Aware Learning
ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs
May 3, 2026Marah Halawa, Olaf HellwichFairness-Aware Learning Contrastive Loss
Exploring Entropy-based Active Learning for Fair Brain Segmentation
May 3, 2026Ghazal Danaee, Mélanie Gaillochet, Christian Desrosiers +2Fairness-Aware Learning Medical Image Segmentation
Fair Dataset Distillation via Cross-Group Barycenter Alignment
Apr 30, 2026Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei +2Fairness-Aware Learning Chain-Of-Thought Distillation
MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Apr 30, 2026Jeanne Monnier, Thomas George, Frédéric Guyard +2Fairness-Aware Learning Multiclass Classification
Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases
Apr 29, 2026Gota MorishitaFederated Learning Fairness-Aware Learning
Efficient and Interpretable Transformer for Counterfactual Fairness
Apr 29, 2026Panyi Dong, Zhiyu QuanFairness-Aware Learning Counterfactual Learning
People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
Apr 28, 2026Zheng Zhang, Milad Masroor, Cuong Nguyen +6Fairness-Aware Learning Medical Image Classification
A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
Apr 25, 2026Daan Rosendal, Ana OprescuFederated Learning Fairness-Aware Learning
FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation
Apr 23, 2026Jinhee Jang, Juhwan Choi, Dongjin Lee +2Machine Translation Quality Gender Bias
Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Apr 20, 2026Junyi Yao, Zihao Zheng, Jiayu LongRanking Perturbations
Fairness Constraints in High-Dimensional Generalized Linear Models
Apr 17, 2026Yixiao Lin, James BoothFairness-Aware Learning Fairness Interventions
Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure
Apr 17, 2026Stefan Behfar, Richard MortierFederated Learning Fairness-Aware Learning
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
Apr 16, 2026He Yang, Dongyi Lv, Wei Xi +3Federated Learning Byzantine Attacks
A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks
Mar 22, 2026Maryam Boubekraoui, Giordano d'Aloisio, Antinisca Di MarcoFairness-Aware Learning Multiclass Classification
Separation-Utility Pareto Frontier: An Information-Theoretic Characterization
Feb 4, 2026Shizhou XuSeparability Privacy-Utility Trade-Offs
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection
Jan 17, 2026Muhammad Alif Al Hakim, Alfan Farizki Wicaksono, Fajri KotoLarge Language Model Quantization Large Language Model Safety
Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
Dec 28, 2025Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail +2Federated Averaging Fairness-Aware Learning
FairLRF: Achieving Fairness through Sparse Low Rank Factorization
Nov 20, 2025Yuanbo Guo, Jun Xia, Yiyu ShiFairness-Aware Learning Bias Mitigation
Attribution Graphs and Causal Probing for Mechanistic Discovery and Bias Repair in Multimodal Generative Learning
Oct 14, 2025Noor Islam S. Mohammad, Uluğ BayazıtTraining Data Attribution Mechanistic Interpretability
MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation
Sep 30, 2025Md Zubair, Hao Zheng, Grayson W. Armstrong +4Robust Multimodal Learning Fairness-Aware Learning
Unbiased Binning for Fairness-aware Attribute Representation
Sep 26, 2025Abolfazl Asudeh, Zeinab Asoodeh, Bita Asoodeh +1Fairness-Aware Learning Differential Privacy
FAIRVAR: Fair Federated Learning via Variance Regularization
Aug 16, 2025Zahra Kharaghani, Ali Dadras, Tommy LöfstedtFairness-Aware Learning Federated Learning
Private Rate-Constrained Optimization with Applications to Fair Learning
May 28, 2025Mohammad Yaghini, Tudor Cebere, Michael Menart +2Differential Privacy Fairness-Aware Learning
Causally Fair Node Classification on Non-IID Graph Data
May 3, 2025Yucong Dai, Lu Zhang, Yaowei Hu +2Fairness-Aware Learning Causal Graph
FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization
Mar 29, 2025Yucong Dai, Jie Ji, Xiaolong Ma +1Fairness-Aware Learning Image Classification
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Fairness-Aware Learning Counterfactual Learning
Perturbation Effects on Robustness and Individual Fairness
Apr 1, 2024Xuran Li, Hao Xue, Peng Wu +4Fairness-Aware Learning Perturbations
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
Feb 2, 2024Pablo Casas, Huan Yu, Christophe MuesFairness-Aware Learning Credit Risk Prediction

May 12, 2026 · cs.LGJ/K move · Enter open · S save

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

Satwat Bashir, Tasos Dagiuklas, Muddesar Iqbal

Computer Science and Informatics · London South Bank University · London, UK · Computer Science and Digital Technologies

Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for jointly optimizing cluster assignment and client selection under data heterogeneity. This paper proposes Fed-BAC, which integrates additive cluster personalization with a two-level bandit framework: contextual bandits at the cloud learn server-to-cluster assignments, while Thompson Sampling at each edge server identifies high-contributing clients. The additive decomposition enables the sharing of knowledge between groups through a globally aggregated network, while cluster-specific networks capture distribution variations. Across three classification benchmarks (CIFAR-10, SVHN, Fashion-MNIST) under moderate (

α= 0.5

) and severe (

α= 0.1

) Dirichlet non-IID partitioning, Fed-BAC achieves distributed accuracy gains of up to +35.5pp over HierFAVG and +8.4pp over IFCA, while requiring only 80% client participation, converging 1.5 to 4.8

\times

faster depending on dataset and accuracy target, and improving cross-server fairness. These gains are further validated at 5

\times

deployment scale on CIFAR-10. The advantage of Fed-BAC increases with heterogeneity severity, confirming that additive cluster personalization becomes increasingly valuable as data distributions diverge.

Heterogeneous Federated Learning Federated Learning Fedavg Image Clustering Heterogeneous Edge Devices Fairness-Aware Learning Distributional Learning Edge Computing

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