Cover Image

A Survey on Federated Unlearning: Challenges and Opportunities

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jeong, Hyejun, Ma, Shiqing, Houmansadr, Amir
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead
by: Nguyen, Thanh Linh, et al.
Published: (2025)

Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
by: Amato, Flora, et al.
Published: (2025)

Federated Unlearning with Gradient Descent and Conflict Mitigation
by: Pan, Zibin, et al.
Published: (2024)

DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
by: Zhuang, Weiming, et al.
Published: (2023)

From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
by: Li, Qiongxiu, et al.
Published: (2025)

Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
by: Shin, Yujin, et al.
Published: (2024)

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
by: Zuo, Xuhan, et al.
Published: (2024)

Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
by: Huynh, Thanh Trung, et al.
Published: (2024)

DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
by: Chang, Ying, et al.
Published: (2025)

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)

Unlearning during Learning: An Efficient Federated Machine Unlearning Method
by: Gu, Hanlin, et al.
Published: (2024)

Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)

Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)

A deep cut into Split Federated Self-supervised Learning
by: Przewięźlikowski, Marcin, et al.
Published: (2024)

A Survey of Resource-efficient LLM and Multimodal Foundation Models
by: Xu, Mengwei, et al.
Published: (2024)

A Survey on Inference Optimization Techniques for Mixture of Experts Models
by: Liu, Jiacheng, et al.
Published: (2024)

Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)

Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)

Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)

Variational Bayes for Federated Continual Learning
by: Yao, Dezhong, et al.
Published: (2024)

On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)

Uncertainty-Aware Explainable Federated Learning
by: Zhang, Yanci, et al.
Published: (2025)

Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)

SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
by: Liu, Xinyang, et al.
Published: (2024)

FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
by: Xu, Haotian, et al.
Published: (2024)

FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
by: Park, Sanghyeon, et al.
Published: (2025)

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
by: Chen, Minghui, et al.
Published: (2025)

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
by: Zhang, Jianqing, et al.
Published: (2025)

A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
by: Zhang, Ruirui, et al.
Published: (2024)

Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
by: Elbakary, Ahmed, et al.
Published: (2024)

On the Byzantine-Resilience of Distillation-Based Federated Learning
by: Roux, Christophe, et al.
Published: (2024)

Federated Graph Learning with Structure Proxy Alignment
by: Fu, Xingbo, et al.
Published: (2024)

One-Shot Federated Learning with Bayesian Pseudocoresets
by: d'Hondt, Tim, et al.
Published: (2024)

Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)

Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)

Federated style aware transformer aggregation of representations
by: Jeon, Mincheol, et al.
Published: (2025)

Privacy in Federated Learning with Spiking Neural Networks
by: Aksu, Dogukan, et al.
Published: (2025)

Clustered Federated Learning with Hierarchical Knowledge Distillation
by: Ahmad, Sabtain, et al.
Published: (2025)