Saved in:
| Main Authors: | Zhang, Meiying, Zhao, Huan, Ebron, Sheldon, Yang, Kan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.04409 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
by: Younesi, Abolfazl, et al.
Published: (2025)
by: Younesi, Abolfazl, et al.
Published: (2025)
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025)
by: Dou, Zhihao, 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)
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
by: Lee, Younghan, et al.
Published: (2024)
by: Lee, Younghan, et al.
Published: (2024)
Celtibero: Robust Layered Aggregation for Federated Learning
by: Molina-Coronado, Borja
Published: (2024)
by: Molina-Coronado, Borja
Published: (2024)
Vertical Federated Learning: Concepts, Advances and Challenges
by: Liu, Yang, et al.
Published: (2022)
by: Liu, Yang, et al.
Published: (2022)
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
by: Zhang, Zhongjian, et al.
Published: (2025)
by: Zhang, Zhongjian, et al.
Published: (2025)
Byzantine-Robust Federated Learning Using Generative Adversarial Networks
by: Zafar, Usama, et al.
Published: (2025)
by: Zafar, Usama, et al.
Published: (2025)
Anomalous Client Detection in Federated Learning
by: Thakur, Dipanwita, et al.
Published: (2024)
by: Thakur, Dipanwita, et al.
Published: (2024)
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
by: Li, Zhiqiang, et al.
Published: (2025)
by: Li, Zhiqiang, et al.
Published: (2025)
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
by: Xu, Yichang, et al.
Published: (2024)
by: Xu, Yichang, 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)
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)
by: Zuo, Xuhan, et al.
Published: (2024)
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)
Federated Graph Condensation with Information Bottleneck Principles
by: Yan, Bo, et al.
Published: (2024)
by: Yan, Bo, et al.
Published: (2024)
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)
FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
by: Jiang, Haocheng, et al.
Published: (2025)
by: Jiang, Haocheng, et al.
Published: (2025)
Federated Unlearning with Gradient Descent and Conflict Mitigation
by: Pan, Zibin, et al.
Published: (2024)
by: Pan, Zibin, 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)
Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
by: Yan, Bo, et al.
Published: (2023)
by: Yan, Bo, et al.
Published: (2023)
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
by: Lai, Shih-Yu, et al.
Published: (2026)
by: Lai, Shih-Yu, et al.
Published: (2026)
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
by: Gong, Zirui, et al.
Published: (2025)
by: Gong, Zirui, et al.
Published: (2025)
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)
Random Client Selection on Contrastive Federated Learning for Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, et al.
Published: (2025)
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)
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)
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
by: Xhemrishi, Marvin, et al.
Published: (2025)
by: Xhemrishi, Marvin, et al.
Published: (2025)
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
by: Alsulaimawi, Zahir
Published: (2024)
by: Alsulaimawi, Zahir
Published: (2024)
FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
by: Liu, Sheng, et al.
Published: (2026)
by: Liu, Sheng, et al.
Published: (2026)
Secure and Privacy-Preserving Vertical Federated Learning
by: Jin, Shan, et al.
Published: (2026)
by: Jin, Shan, et al.
Published: (2026)
Trustworthy Federated Learning: Privacy, Security, and Beyond
by: Chen, Chunlu, et al.
Published: (2024)
by: Chen, Chunlu, et al.
Published: (2024)
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)
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)
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
by: Penalva, Isaac Marroqui, et al.
Published: (2025)
by: Penalva, Isaac Marroqui, et al.
Published: (2025)
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
by: Liu, Yu, et al.
Published: (2024)
by: Liu, Yu, et al.
Published: (2024)
An Adaptive Differential Privacy Method Based on Federated Learning
by: Wang, Zhiqiang, et al.
Published: (2024)
by: Wang, Zhiqiang, et al.
Published: (2024)
Similar Items
-
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) -
FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
by: Younesi, Abolfazl, et al.
Published: (2025) -
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025) -
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
by: Zhang, Jianing, et al.
Published: (2025) -
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
by: Lee, Younghan, et al.
Published: (2024)