Copertina

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shao, Jiaqi, Han, Shanshan, He, Chaoyang, Luo, Bing
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

BFLN: A Blockchain-based Federated Learning Model for Non-IID Data
di: Li, Yang, et al.
Pubblicazione: (2024)

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
di: Ng, Jer Shyuan, et al.
Pubblicazione: (2025)

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
di: Wang, Shilong, et al.
Pubblicazione: (2025)

Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
di: Chen, Shuaijun, et al.
Pubblicazione: (2023)

Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling
di: Will, Jonathan, et al.
Pubblicazione: (2024)

Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics
di: Geng, Jiaxiang, et al.
Pubblicazione: (2024)

Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics
di: Sarraf, Gaurav, et al.
Pubblicazione: (2026)

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

Understanding Communication Backends in Cross-Silo Federated Learning
di: Ziashahabi, Amir, et al.
Pubblicazione: (2026)

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
di: Mujtaba, Ahmed, et al.
Pubblicazione: (2025)

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
di: Islam, Md Sirajul, et al.
Pubblicazione: (2024)

Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
di: Makkonen, Okko, et al.
Pubblicazione: (2024)

Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
di: Li, Zilinghan, et al.
Pubblicazione: (2025)

Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
di: Cox, Bart, et al.
Pubblicazione: (2026)

Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
di: Morafah, Mahdi, et al.
Pubblicazione: (2024)

DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
di: Han, Baofu, et al.
Pubblicazione: (2025)

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
di: Lee, Seunghyun, et al.
Pubblicazione: (2025)

CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models
di: Yi, Mengjun, et al.
Pubblicazione: (2025)

Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
di: Yu, Minchen, et al.
Pubblicazione: (2025)

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
di: Yang, Yuwen, et al.
Pubblicazione: (2024)

Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data
di: Feng, Chao, et al.
Pubblicazione: (2024)

Privacy-Preserving and Trustworthy Localization in an IoT Environment
di: Zocca, Guglielmo, et al.
Pubblicazione: (2024)

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

Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data
di: Ahn, Seyoung, et al.
Pubblicazione: (2022)

Privacy-Preserving Coding Schemes for Multi-Access Distributed Computing Models
di: Sasi, Shanuja
Pubblicazione: (2026)

A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon
di: Süwer, Simon, et al.
Pubblicazione: (2025)

A Privacy-Preserving Machine Learning Framework for Edge Intelligence: An Empirical Analysis
di: Trieu, Quoc Lap, et al.
Pubblicazione: (2026)

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
di: Jimenez-Gutierrez, Daniel M., et al.
Pubblicazione: (2026)

Privacy-Preserving and Incentive-Driven Relay-Based Framework for Cross-Domain Blockchain Interoperability
di: Moradi, Saeed, et al.
Pubblicazione: (2025)

Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification
di: Tanbhir, Gazi, et al.
Pubblicazione: (2025)

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

Federated Neural Radiance Field for Distributed Intelligence
di: Zhang, Yintian, et al.
Pubblicazione: (2024)

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
di: Chunduru, Abhijit, et al.
Pubblicazione: (2025)

Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
di: Ravikumar, Deepak, et al.
Pubblicazione: (2023)

Crossword: Adaptive Consensus for Dynamic Data-Heavy Workloads
di: Hu, Guanzhou, et al.
Pubblicazione: (2025)

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
di: AbouNassar, Eman M., et al.
Pubblicazione: (2026)

Differential Privacy Preserving Distributed Quantum Computing
di: Zhong, Hui, et al.
Pubblicazione: (2024)

Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning
di: Li, Naicheng, et al.
Pubblicazione: (2026)

Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures
di: Gupta, Divya
Pubblicazione: (2026)

Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
di: Madduri, Ravi, et al.
Pubblicazione: (2024)