Cover Image

FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xiao, Chenguang, Zuo, Zheming, Wang, Shuo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedGIG: Graph Inversion from Gradient in Federated Learning
by: Xiao, Tianzhe, et al.
Published: (2024)

FedRE: Robust and Effective Federated Learning with Privacy Preference
by: Xiao, Tianzhe, et al.
Published: (2025)

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
by: Fereidooni, Hossein, et al.
Published: (2023)

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
by: Zhu, Shunan, et al.
Published: (2026)

FedPID: An Aggregation Method for Federated Learning
by: Mächler, Leon, et al.
Published: (2024)

FedQV: Leveraging Quadratic Voting in Federated Learning
by: Chu, Tianyue, et al.
Published: (2024)

FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
by: Yan, Xinhai, et al.
Published: (2025)

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
by: Wang, Zhichao, et al.
Published: (2024)

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
by: Shao, Shuo, et al.
Published: (2022)

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
by: Tan, Zihan, et al.
Published: (2024)

FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
by: Zhang, Xiangyu, et al.
Published: (2025)

GShield: Mitigating Poisoning Attacks in Federated Learning
by: M., Sameera K., et al.
Published: (2025)

Mitigating Error Amplification in Fast Adversarial Training
by: Zhao, Mengnan, et al.
Published: (2026)

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
by: Lu, Bingguang, et al.
Published: (2025)

FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
by: Abacha, Fatima Z., et al.
Published: (2026)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
by: Jung, Kangsoo, et al.
Published: (2025)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
by: Campos, Enrique Mármol, et al.
Published: (2024)

FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
by: Zhu, Gongxi, et al.
Published: (2024)

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
by: Mulay, Ajinkya Kiran, et al.
Published: (2024)

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs
by: Meerza, Syed Irfan Ali, et al.
Published: (2026)

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
by: Jin, Weizhao, et al.
Published: (2023)

FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services
by: Ek, Sannara, et al.
Published: (2024)

Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning
by: Zhang, Rui, et al.
Published: (2024)

FedSpaLLM: Federated Pruning of Large Language Models
by: Bai, Guangji, et al.
Published: (2024)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
by: Rong, Dazhong, et al.
Published: (2022)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
by: Li, Youpeng, et al.
Published: (2024)

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
by: Yang, He, et al.
Published: (2026)

FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
by: Zeng, Zhihao, et al.
Published: (2025)

DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
by: Xue, Jiaqi, et al.
Published: (2025)

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
by: Li, Chengxi, et al.
Published: (2024)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
by: Gong, Zirui, et al.
Published: (2026)

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
by: Alsenani, Yousef, et al.
Published: (2024)

Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
by: Jin, Huitong, et al.
Published: (2024)

FedPoP: Federated Learning Meets Proof of Participation
by: İşler, Devriş, et al.
Published: (2025)

Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
by: Kim, Joon, et al.
Published: (2024)

Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
by: Chen, Xiaolin, et al.
Published: (2021)

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
by: Yi, Kai, et al.
Published: (2024)