Copertina

Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hsu, Chih-Fan, Chang, Ming-Ching, Chen, Wei-Chao
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Continual Learning with Synthetic Boundary Experience Blending
di: Hsu, Chih-Fan, et al.
Pubblicazione: (2025)

A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
di: Li, Jeng-Lin, et al.
Pubblicazione: (2024)

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
di: Chang, Ching, et al.
Pubblicazione: (2023)

Text2Freq: Learning Series Patterns from Text via Frequency Domain
di: Lo, Ming-Chih, et al.
Pubblicazione: (2024)

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

Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis
di: Shiu, Chun Hei Michael, et al.
Pubblicazione: (2026)

COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
di: Ou, Ting-Yun, et al.
Pubblicazione: (2024)

FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data
di: Chen, Yue, et al.
Pubblicazione: (2025)

In-depth Analysis of Privacy Threats in Federated Learning for Medical Data
di: Das, Badhan Chandra, et al.
Pubblicazione: (2024)

Sharpness-Aware Geometric Defense for Robust Out-Of-Distribution Detection
di: Li, Jeng-Lin, et al.
Pubblicazione: (2025)

Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data
di: Hsu, I-Jung, et al.
Pubblicazione: (2025)

Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
di: Ling, Chih Wei, et al.
Pubblicazione: (2025)

DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
di: Han, Yizhou, et al.
Pubblicazione: (2026)

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
di: Das, Badhan Chandra, et al.
Pubblicazione: (2023)

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
di: Zhang, Xiaojin, et al.
Pubblicazione: (2024)

PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
di: Chang, Ching, et al.
Pubblicazione: (2025)

Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
di: Zhang, Yiwei, et al.
Pubblicazione: (2025)

Mitigating Data Injection Attacks on Federated Learning
di: Shalom, Or, et al.
Pubblicazione: (2023)

Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data
di: Xie, Anzhuo, et al.
Pubblicazione: (2025)

Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
di: Li, Jeng-Lin, et al.
Pubblicazione: (2024)

Detecting and Ranking Causal Anomalies in End-to-End Complex System
di: Chang, Ching, et al.
Pubblicazione: (2023)

From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning
di: Xu, Xiangrui, et al.
Pubblicazione: (2025)

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

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
di: Li, Hangyu, et al.
Pubblicazione: (2025)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
di: Han, Pengchao, et al.
Pubblicazione: (2024)

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

Quantum Embedding with Transformer for High-dimensional Data
di: Chen, Hao-Yuan, et al.
Pubblicazione: (2024)

Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue
di: Brunner, David, et al.
Pubblicazione: (2024)

Training Fair Models in Federated Learning without Data Privacy Infringement
di: Che, Xin, et al.
Pubblicazione: (2021)

Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
di: Chang, Zhipeng, et al.
Pubblicazione: (2026)

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
di: Xu, Yichang, et al.
Pubblicazione: (2024)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
di: Xu, Yukai, et al.
Pubblicazione: (2024)

Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition
di: Chen, Wei, et al.
Pubblicazione: (2025)

Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion
di: Luo, Linfeng, et al.
Pubblicazione: (2024)

Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
di: Wei, Dongyu, et al.
Pubblicazione: (2025)

MemPromptTSS: Persistent Prompt Memory for Iterative Multi-Granularity Time Series State Segmentation
di: Chang, Ching, et al.
Pubblicazione: (2025)

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
di: Rokvic, Ljubomir, et al.
Pubblicazione: (2022)

Data Poisoning and Leakage Analysis in Federated Learning
di: Wei, Wenqi, et al.
Pubblicazione: (2024)

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
di: Hiniduma, Kaveen, et al.
Pubblicazione: (2025)

Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
di: Khalil, Mohammad, et al.
Pubblicazione: (2025)