Saved in:
| Main Authors: | Alipour, Mohammadsajad, Amiri, Mohammad Mohammadi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22149 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
by: Herath, Kavindu, et al.
Published: (2026)
by: Herath, Kavindu, et al.
Published: (2026)
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)
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
by: Li, Qilei, et al.
Published: (2024)
by: Li, Qilei, et al.
Published: (2024)
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
by: Quan, Minh K., et al.
Published: (2024)
by: Quan, Minh K., et al.
Published: (2024)
A Framework for Double-Blind Federated Adaptation of Foundation Models
by: Tastan, Nurbek, et al.
Published: (2025)
by: Tastan, Nurbek, et al.
Published: (2025)
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
by: Hou, Charlie, et al.
Published: (2024)
by: Hou, Charlie, et al.
Published: (2024)
Anomalous Client Detection in Federated Learning
by: Thakur, Dipanwita, et al.
Published: (2024)
by: Thakur, Dipanwita, et al.
Published: (2024)
Federated Learning Client Pruning for Noisy Labels
by: Morafah, Mahdi, et al.
Published: (2024)
by: Morafah, Mahdi, et al.
Published: (2024)
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)
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025)
by: Dou, Zhihao, et al.
Published: (2025)
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
by: Oh, Seungeun, et al.
Published: (2024)
by: Oh, Seungeun, et al.
Published: (2024)
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)
Vertical Federated Learning: Concepts, Advances and Challenges
by: Liu, Yang, et al.
Published: (2022)
by: Liu, Yang, et al.
Published: (2022)
Random Client Selection on Contrastive Federated Learning for Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, 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)
Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directions
by: Ghafourian, Mahdi, et al.
Published: (2023)
by: Ghafourian, Mahdi, et al.
Published: (2023)
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 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)
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
by: Lee, Younghan, et al.
Published: (2024)
by: Lee, Younghan, et al.
Published: (2024)
When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMs
by: Wei, Jiankun, et al.
Published: (2024)
by: Wei, Jiankun, 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)
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)
CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning
by: DiMaggio, Anthony, et al.
Published: (2025)
by: DiMaggio, Anthony, et al.
Published: (2025)
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)
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)
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)
Towards Reversible Model Merging For Low-rank Weights
by: Alipour, Mohammadsajad, et al.
Published: (2025)
by: Alipour, Mohammadsajad, 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)
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)
FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol
by: Nikolakakis, Konstantinos E., et al.
Published: (2024)
by: Nikolakakis, Konstantinos E., 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)
Federated Graph Condensation with Information Bottleneck Principles
by: Yan, Bo, et al.
Published: (2024)
by: Yan, Bo, et al.
Published: (2024)
Federated Unlearning with Gradient Descent and Conflict Mitigation
by: Pan, Zibin, et al.
Published: (2024)
by: Pan, Zibin, et al.
Published: (2024)
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)
Federated PCA on Grassmann Manifold for IoT Anomaly Detection
by: Nguyen, Tung-Anh, et al.
Published: (2024)
by: Nguyen, Tung-Anh, et al.
Published: (2024)
Similar Items
-
Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
by: Herath, Kavindu, et al.
Published: (2026) -
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
by: Zhang, Meiying, et al.
Published: (2024) -
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
by: Li, Qilei, et al.
Published: (2024) -
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
by: Quan, Minh K., et al.
Published: (2024) -
A Framework for Double-Blind Federated Adaptation of Foundation Models
by: Tastan, Nurbek, et al.
Published: (2025)