FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Sheng, Papadimitratos, Panos |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
by: Wu, Di, et al.
Published: (2025)
by: Wu, Di, et al.
Published: (2025)
Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)
by: Wang, Wenbin, et al.
Published: (2025)
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
by: Ebron, Sheldon C., et al.
Published: (2023)
by: Ebron, Sheldon C., et al.
Published: (2023)
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
by: Tyagi, Sahil, et al.
Published: (2025)
by: Tyagi, Sahil, et al.
Published: (2025)
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning
by: Gan, Xiaoyun, et al.
Published: (2024)
by: Gan, Xiaoyun, et al.
Published: (2024)
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
by: Dao, Thinh, et al.
Published: (2025)
by: Dao, Thinh, et al.
Published: (2025)
Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
by: Mo, Wenjin, et al.
Published: (2025)
by: Mo, Wenjin, et al.
Published: (2025)
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
by: Lari, Ehsan, et al.
Published: (2024)
by: Lari, Ehsan, et al.
Published: (2024)
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
by: Zhang, Hangtao, et al.
Published: (2023)
by: Zhang, Hangtao, et al.
Published: (2023)
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)
by: Tan, Qi, et al.
Published: (2024)
FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
by: Turazza, Fabio, et al.
Published: (2026)
by: Turazza, Fabio, et al.
Published: (2026)
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
by: Zhou, Anqi, et al.
Published: (2024)
by: Zhou, Anqi, et al.
Published: (2024)
Poisoning with A Pill: Circumventing Detection in Federated Learning
by: Guo, Hanxi, et al.
Published: (2024)
by: Guo, Hanxi, et al.
Published: (2024)
PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
by: Sun, Wei, et al.
Published: (2026)
by: Sun, Wei, et al.
Published: (2026)
Vertical Federated Learning: Concepts, Advances and Challenges
by: Liu, Yang, et al.
Published: (2022)
by: Liu, Yang, et al.
Published: (2022)
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
by: Syros, Georgios, et al.
Published: (2025)
by: Syros, Georgios, et al.
Published: (2025)
Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems
by: Yan, Bo, et al.
Published: (2025)
by: Yan, Bo, et al.
Published: (2025)
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
by: Nguyen, Manh V., et al.
Published: (2025)
by: Nguyen, Manh V., et al.
Published: (2025)
PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
by: Zhang, Hongliang, et al.
Published: (2025)
by: Zhang, Hongliang, et al.
Published: (2025)
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
by: Zhang, Jianing, et al.
Published: (2025)
by: Zhang, Jianing, et al.
Published: (2025)
Inclusive, Differentially Private Federated Learning for Clinical Data
by: Parampottupadam, Santhosh, et al.
Published: (2025)
by: Parampottupadam, Santhosh, et al.
Published: (2025)
BAFFLE: A Baseline of Backpropagation-Free Federated Learning
by: Feng, Haozhe, et al.
Published: (2023)
by: Feng, Haozhe, et al.
Published: (2023)
On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation
by: Alhaidari, Abdulrahman, et al.
Published: (2025)
by: Alhaidari, Abdulrahman, et al.
Published: (2025)
Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
by: Liu, Ji, et al.
Published: (2024)
by: Liu, Ji, et al.
Published: (2024)
Federated Learning for Cyber Physical Systems: A Comprehensive Survey
by: Quan, Minh K., et al.
Published: (2025)
by: Quan, Minh K., et al.
Published: (2025)
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
by: Ozfatura, Kerem, et al.
Published: (2022)
by: Ozfatura, Kerem, et al.
Published: (2022)
SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning
by: Li, Borui, et al.
Published: (2025)
by: Li, Borui, et al.
Published: (2025)
FedCert: Federated Accuracy Certification
by: Nguyen, Minh Hieu, et al.
Published: (2024)
by: Nguyen, Minh Hieu, et al.
Published: (2024)
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
POPri: Private Federated Learning using Preference-Optimized Synthetic Data
by: Hou, Charlie, et al.
Published: (2025)
by: Hou, Charlie, et al.
Published: (2025)
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
by: Akhtarshenas, Azim, et al.
Published: (2023)
by: Akhtarshenas, Azim, et al.
Published: (2023)
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
by: Zhang, Meiying, et al.
Published: (2024)
by: Zhang, Meiying, et al.
Published: (2024)
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
by: Lee, Younghan, et al.
Published: (2024)
by: Lee, Younghan, et al.
Published: (2024)
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
by: Bouzinis, Pavlos S., et al.
Published: (2024)
by: Bouzinis, Pavlos S., et al.
Published: (2024)
Federated Graph Condensation with Information Bottleneck Principles
by: Yan, Bo, et al.
Published: (2024)
by: Yan, Bo, et al.
Published: (2024)
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
by: Alsulaimawi, Zahir
Published: (2024)
by: Alsulaimawi, Zahir
Published: (2024)
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)
by: Zhang, Jianqing, et al.
Published: (2024)
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
FedAT: Federated Adversarial Training for Distributed Insider Threat Detection
by: Gayathri, R G, et al.
Published: (2024)
by: Gayathri, R G, et al.
Published: (2024)
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
by: Puppala, Sai, et al.
Published: (2024)
by: Puppala, Sai, et al.
Published: (2024)
Similar Items
-
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
by: Wu, Di, et al.
Published: (2025) -
Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025) -
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
by: Ebron, Sheldon C., et al.
Published: (2023) -
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
by: Tyagi, Sahil, et al.
Published: (2025) -
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning
by: Gan, Xiaoyun, et al.
Published: (2024)