Saved in:
| Main Authors: | Tadi, Ali Abbasi, Alhadidi, Dima, Rueda, Luis |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.11706 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
by: Palit, Vedant
Published: (2025)
by: Palit, Vedant
Published: (2025)
Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks
by: Elmahallawy, Mohamed, et al.
Published: (2025)
by: Elmahallawy, Mohamed, et al.
Published: (2025)
Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions
by: Ganescu, Bianca-Mihaela, et al.
Published: (2024)
by: Ganescu, Bianca-Mihaela, et al.
Published: (2024)
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
by: Hallaji, Ehsan, et al.
Published: (2025)
by: Hallaji, Ehsan, et al.
Published: (2025)
ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
by: Li, Yang, et al.
Published: (2025)
by: Li, Yang, et al.
Published: (2025)
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
by: Lowy, Andrew, et al.
Published: (2021)
by: Lowy, Andrew, et al.
Published: (2021)
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
by: Raza, Ali, et al.
Published: (2022)
by: Raza, Ali, et al.
Published: (2022)
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
by: Kumar, Abhinav, et al.
Published: (2025)
by: Kumar, Abhinav, et al.
Published: (2025)
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
by: Campos, Enrique Mármol, et al.
Published: (2024)
by: Campos, Enrique Mármol, et al.
Published: (2024)
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data
by: Long, Yunbo, et al.
Published: (2025)
by: Long, Yunbo, et al.
Published: (2025)
Personalized Federated Learning Techniques: Empirical Analysis
by: Khan, Azal Ahmad, et al.
Published: (2024)
by: Khan, Azal Ahmad, et al.
Published: (2024)
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
by: Munir, Md. Shirajum, et al.
Published: (2024)
by: Munir, Md. Shirajum, et al.
Published: (2024)
A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation
by: Arazzi, Marco, et al.
Published: (2023)
by: Arazzi, Marco, et al.
Published: (2023)
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
by: Wu, Zhaomin, et al.
Published: (2024)
by: Wu, Zhaomin, et al.
Published: (2024)
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
by: Ali, Mahad, et al.
Published: (2025)
by: Ali, Mahad, et al.
Published: (2025)
BlocksecRT-DETR: Decentralized Privacy-Preserving and Token-Efficient Federated Transformer Learning for Secure Real-Time Object Detection in ITS
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
by: Tahera, Mohoshin Ara, et al.
Published: (2026)
Can Features for Phishing URL Detection Be Trusted Across Diverse Datasets? A Case Study with Explainable AI
by: Mia, Maraz, et al.
Published: (2024)
by: Mia, Maraz, et al.
Published: (2024)
Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias
by: Bernstein, Shir, et al.
Published: (2025)
by: Bernstein, Shir, et al.
Published: (2025)
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
by: Jin, Heng, et al.
Published: (2026)
by: Jin, Heng, et al.
Published: (2026)
Confidential Federated Computations
by: Eichner, Hubert, et al.
Published: (2024)
by: Eichner, Hubert, et al.
Published: (2024)
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
by: Gao, Changyu, et al.
Published: (2024)
by: Gao, Changyu, et al.
Published: (2024)
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
by: Lan, Tianwei, et al.
Published: (2025)
by: Lan, Tianwei, et al.
Published: (2025)
Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments
by: Agarwal, Dhruv Deepak, et al.
Published: (2025)
by: Agarwal, Dhruv Deepak, et al.
Published: (2025)
Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)
by: Fu, Jie, et al.
Published: (2024)
Attacks on fairness in Federated Learning
by: Rance, Joseph, et al.
Published: (2023)
by: Rance, Joseph, et al.
Published: (2023)
Federated Bayesian Network Ensembles
by: van Daalen, Florian, et al.
Published: (2024)
by: van Daalen, Florian, et al.
Published: (2024)
Federated Learning with Relative Fairness
by: Nakakita, Shogo, et al.
Published: (2024)
by: Nakakita, Shogo, et al.
Published: (2024)
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
by: Pulido-Cortázar, Daniel, et al.
Published: (2025)
by: Pulido-Cortázar, Daniel, et al.
Published: (2025)
Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
by: Ye, Mang, et al.
Published: (2024)
by: Ye, Mang, et al.
Published: (2024)
Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction
by: Chaudhary, Ayush, et al.
Published: (2025)
by: Chaudhary, Ayush, et al.
Published: (2025)
Federated Computation of ROC and PR Curves
by: Xu, Xuefeng, et al.
Published: (2025)
by: Xu, Xuefeng, et al.
Published: (2025)
Federated Unlearning for Human Activity Recognition
by: Chen, Kongyang, et al.
Published: (2024)
by: Chen, Kongyang, et al.
Published: (2024)
Goldfish: An Efficient Federated Unlearning Framework
by: Wang, Houzhe, et al.
Published: (2024)
by: Wang, Houzhe, et al.
Published: (2024)
On the Efficiency of Privacy Attacks in Federated Learning
by: Tabassum, Nawrin, et al.
Published: (2024)
by: Tabassum, Nawrin, et al.
Published: (2024)
Model Hijacking Attack in Federated Learning
by: Li, Zheng, et al.
Published: (2024)
by: Li, Zheng, et al.
Published: (2024)
Blockchain-enabled Trustworthy Federated Unlearning
by: Lin, Yijing, et al.
Published: (2024)
by: Lin, Yijing, et al.
Published: (2024)
Location Leakage in Federated Signal Maps
by: Bakopoulou, Evita, et al.
Published: (2021)
by: Bakopoulou, Evita, et al.
Published: (2021)
Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)
by: Nguyen, Truc, et al.
Published: (2022)
Private Federated Learning In Real World Application -- A Case Study
by: Ji, An, et al.
Published: (2025)
by: Ji, An, et al.
Published: (2025)
A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)
by: Guo, Pengxin, et al.
Published: (2024)
Similar Items
-
Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
by: Palit, Vedant
Published: (2025) -
Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks
by: Elmahallawy, Mohamed, et al.
Published: (2025) -
Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions
by: Ganescu, Bianca-Mihaela, et al.
Published: (2024) -
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
by: Hallaji, Ehsan, et al.
Published: (2025) -
ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
by: Li, Yang, et al.
Published: (2025)