Copertina

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wu, Zihou, Li, Yuecheng, Liao, Tianchi, Lou, Jian, Chen, Chuan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
di: Wen, Zhenyu, et al.
Pubblicazione: (2024)

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models
di: Wang, Yujia, et al.
Pubblicazione: (2025)

Improving LoRA in Privacy-preserving Federated Learning
di: Sun, Youbang, et al.
Pubblicazione: (2024)

FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
di: Xia, Tong, et al.
Pubblicazione: (2024)

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
di: Duan, Moming, et al.
Pubblicazione: (2021)

Vertical Federated Learning: Challenges, Methodologies and Experiments
di: Wei, Kang, et al.
Pubblicazione: (2022)

Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
di: Zhao, Xiyu, et al.
Pubblicazione: (2025)

BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
di: Zhang, Xianzhi, et al.
Pubblicazione: (2024)

FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
di: Wang, Yujie, et al.
Pubblicazione: (2024)

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
di: Dhasade, Akash, et al.
Pubblicazione: (2024)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
di: Xu, Ruichen, et al.
Pubblicazione: (2026)

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
di: Yuan, Tianjun, et al.
Pubblicazione: (2025)

Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
di: Yan, Bo, et al.
Pubblicazione: (2023)

Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
di: Zaland, Obaidullah, et al.
Pubblicazione: (2026)

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
di: Bian, Jieming, et al.
Pubblicazione: (2024)

Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
di: Luo, Kangyang, et al.
Pubblicazione: (2024)

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
di: Li, Jian, et al.
Pubblicazione: (2024)

MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
di: Li, Xin-Chun, et al.
Pubblicazione: (2024)

FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
di: Liu, Tao, et al.
Pubblicazione: (2026)

Robust and Communication-Efficient Federated Domain Adaptation via Random Features
di: Feng, Zhanbo, et al.
Pubblicazione: (2023)

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
di: Chen, Dong, et al.
Pubblicazione: (2024)

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
di: Jiang, Chenyu, et al.
Pubblicazione: (2025)

FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
di: Zhou, Zihao, et al.
Pubblicazione: (2025)

FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
di: Xia, Tong, et al.
Pubblicazione: (2023)

Decentralized Federated Learning with Model Caching on Mobile Agents
di: Wang, Xiaoyu, et al.
Pubblicazione: (2024)

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
di: Zhao, Xiyu, et al.
Pubblicazione: (2025)

Combining Cloud and Mobile Computing for Machine Learning
di: Xu, Ruiqi, et al.
Pubblicazione: (2024)

Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training
di: Wu, Yebo, et al.
Pubblicazione: (2024)

Federated Graph Learning with Graphless Clients
di: Fu, Xingbo, et al.
Pubblicazione: (2024)

Sketched Gaussian Mechanism for Private Federated Learning
di: Li, Qiaobo, et al.
Pubblicazione: (2025)

FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
di: Ambekar, Siddharth, et al.
Pubblicazione: (2024)

SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning
di: Yang, Yuning, et al.
Pubblicazione: (2024)

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

FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
di: Zhan, Ziwei, et al.
Pubblicazione: (2024)

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
di: Chen, Hongyao, et al.
Pubblicazione: (2025)

FedLog: Personalized Federated Classification with Less Communication and More Flexibility
di: Yu, Haolin, et al.
Pubblicazione: (2024)

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
di: Tian, Chunlin, et al.
Pubblicazione: (2024)

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
di: Xu, Yang, et al.
Pubblicazione: (2023)

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
di: Yang, Xu, et al.
Pubblicazione: (2024)

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
di: Feng, Zhiying, et al.
Pubblicazione: (2023)