:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Madabushi, Sindhuja, Khan, Ahmad Faraz, Ali, Haider, Swami, Ananthram, Ning, Rui, Wu, Hongyi, Cho, Jin-Hee
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2512.12840
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning
di: Madabushi, Sindhuja, et al.
Pubblicazione: (2025)

MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learning
di: Madabushi, Sindhuja, et al.
Pubblicazione: (2025)

Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
di: Wasif, Dawood, et al.
Pubblicazione: (2025)

Personalized Federated Learning Techniques: Empirical Analysis
di: Khan, Azal Ahmad, et al.
Pubblicazione: (2024)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
di: Varasteh, Morteza
Pubblicazione: (2023)

FlowSymm: Physics Aware, Symmetry Preserving Graph Attention for Network Flow Completion
di: Demirci, Ege, et al.
Pubblicazione: (2026)

Topology Identification and Inference over Graphs
di: Mateos, Gonzalo, et al.
Pubblicazione: (2025)

IP-FL: Incentivized and Personalized Federated Learning
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2023)

AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
di: Madabushi, Sindhuja, et al.
Pubblicazione: (2026)

Biased Backpressure Routing Using Link Features and Graph Neural Networks
di: Zhao, Zhongyuan, et al.
Pubblicazione: (2024)

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2022)

Learning-based Privacy-Preserving Graph Publishing Against Sensitive Link Inference Attacks
di: Wu, Yucheng, et al.
Pubblicazione: (2025)

Secure and Privacy-Preserving Vertical Federated Learning
di: Jin, Shan, et al.
Pubblicazione: (2026)

Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges
di: Sayyed, Sazzad, et al.
Pubblicazione: (2024)

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
di: Nilsson, Ivo Osterberg, et al.
Pubblicazione: (2026)

When not to target negative ties? Studying competitive influence maximisation in signed networks
di: Chakraborty, Sukankana, et al.
Pubblicazione: (2025)

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
di: Zhao, Zhongyuan, et al.
Pubblicazione: (2025)

FLStore: Efficient Federated Learning Storage for non-training workloads
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2025)

Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences
di: Shi, Ming, et al.
Pubblicazione: (2026)

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
di: Arazzi, Marco, et al.
Pubblicazione: (2024)

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility
di: Wasif, Dawood, et al.
Pubblicazione: (2025)

Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation
di: Laskurain, Unai, et al.
Pubblicazione: (2025)

Learning Non-myopic Power Allocation in Constrained Scenarios
di: Chowdhury, Arindam, et al.
Pubblicazione: (2024)

To Offload or Not To Offload: Model-driven Comparison of Edge-native and On-device Processing In the Era of Accelerators
di: Ng, Nathan, et al.
Pubblicazione: (2025)

SeLR: Sparsity-enhanced Lagrangian Relaxation for Computation Offloading at the Edge
di: Erfaniantaghvayi, Negar, et al.
Pubblicazione: (2025)

A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking
di: Zhao, Zhongyuan, et al.
Pubblicazione: (2025)

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
di: Liu, Jin, et al.
Pubblicazione: (2026)

EcoFair: Trustworthy and Energy-Aware Routing for Privacy-Preserving Vertically Partitioned Medical Inference
di: Anoosha, Mostafa, et al.
Pubblicazione: (2026)

LADs: Leveraging LLMs for AI-Driven DevOps
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2025)

AI‐Driven Adaptive Federated Learning With Privacy Preservation and Imbalance Adjustment for Financial Credit Card Fraud Detection
di: Muhammad Shoaib Farooq, et al.
Pubblicazione: (2025)

FLASH: Federated Learning Across Simultaneous Heterogeneities
di: Chang, Xiangyu, et al.
Pubblicazione: (2024)

Bayes-Nash Generative Privacy Against Membership Inference Attacks
di: Zhang, Tao, et al.
Pubblicazione: (2024)

Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud
di: Higgins, Griffin, et al.
Pubblicazione: (2025)

Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning
di: Wasif, Dawood, et al.
Pubblicazione: (2026)

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
di: Xie, Chulin, et al.
Pubblicazione: (2022)

Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
di: Chen, Jinyin, et al.
Pubblicazione: (2024)

Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
di: Chang, Xiangyu, et al.
Pubblicazione: (2025)

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
di: Tran, Linh, et al.
Pubblicazione: (2025)

Link-Sharing Backpressure Routing In Wireless Multi-Hop Networks
di: Zhao, Zhongyuan, et al.
Pubblicazione: (2025)

Fully Distributed Online Training of Graph Neural Networks in Networked Systems
di: Olshevskyi, Rostyslav, et al.
Pubblicazione: (2024)