Cover Image

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yadav, Sushilkumar, Bor-Yaliniz, Irem
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
by: Xu, Yukai, et al.
Published: (2024)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
by: Dong, Wenhan, et al.
Published: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
by: Ghosh, Bishwamittra, et al.
Published: (2024)

Privacy-Preserving in Blockchain-based Federated Learning Systems
by: M., Sameera K., et al.
Published: (2024)

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
by: Scheliga, Daniel, et al.
Published: (2023)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)

Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
by: Wu, Leming, et al.
Published: (2025)

Privacy-Preserving Dynamic Assortment Selection
by: Cho, Young Hyun, et al.
Published: (2024)

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
by: Hiniduma, Kaveen, et al.
Published: (2025)

Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
by: Khalil, Mohammad, et al.
Published: (2025)

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
by: Yu, Sixing, et al.
Published: (2023)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
by: Li, Yuecheng, et al.
Published: (2024)

Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
by: Arazzi, Marco, et al.
Published: (2025)

Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
by: Ge, Shiming, et al.
Published: (2024)

Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
by: Seo, Jungwon, et al.
Published: (2026)

FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning
by: Gu, Chen, et al.
Published: (2025)

Client-Side Patching against Backdoor Attacks in Federated Learning
by: Molina-Coronado, Borja
Published: (2024)

EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
by: Meerza, Syed Irfan Ali, et al.
Published: (2024)

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
by: Rokvic, Ljubomir, et al.
Published: (2022)

C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
by: Kim, Yeachan, et al.
Published: (2024)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
by: Yuan, Jinsheng, et al.
Published: (2025)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023)

Federated Learning With Individualized Privacy Through Client Sampling
by: Lange, Lucas, et al.
Published: (2025)

Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
by: Zhou, Xinjie, et al.
Published: (2026)

Privacy-Preserving Intrusion Detection using Convolutional Neural Networks
by: Kodys, Martin, et al.
Published: (2024)

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
by: Islam, MD Shafikul, et al.
Published: (2026)

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning
by: Maheri, Mohammad Mahdi, et al.
Published: (2025)

Tackling the Local Bias in Federated Graph Learning
by: Zhang, Binchi, et al.
Published: (2021)

Decentralized Federated Learning: A Survey on Security and Privacy
by: Hallaji, Ehsan, et al.
Published: (2024)

TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
by: Maheri, Mohammad M, et al.
Published: (2025)

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
by: Chandrinos, Nikolaos, et al.
Published: (2024)

FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning
by: Shuvo, Md Nahid Hasan, et al.
Published: (2025)

MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
by: Jiang, Weisen, et al.
Published: (2026)

SoK: Security and Privacy Risks of Healthcare AI
by: Chang, Yuanhaur, et al.
Published: (2024)

Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
by: Sun, Rui, et al.
Published: (2024)

PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites
by: Kim, Jaehan, et al.
Published: (2025)

Privacy Assessment of Federated Learning using Private Personalized Layers
by: Jourdan, Théo, et al.
Published: (2021)

Federated Split Learning for Human Activity Recognition with Differential Privacy
by: Ndeko, Josue, et al.
Published: (2024)

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
by: Lee, Joohyung, et al.
Published: (2023)

Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
by: Wu, Hangyu
Published: (2026)