Image de couverture de livre

Federated Learning in Practice: Reflections and Projections

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Daly, Katharine, Eichner, Hubert, Kairouz, Peter, McMahan, H. Brendan, Ramage, Daniel, Xu, Zheng
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Confidential Federated Computations
par: Eichner, Hubert, et autres
Publié: (2024)

One-shot Empirical Privacy Estimation for Federated Learning
par: Andrew, Galen, et autres
Publié: (2023)

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
par: Ponomareva, Natalia, et autres
Publié: (2025)

Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
par: Shumailov, Ilia, et autres
Publié: (2025)

An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy
par: McMahan, H. Brendan, et autres
Publié: (2025)

On Design Principles for Private Adaptive Optimizers
par: Ganesh, Arun, et autres
Publié: (2025)

MAPLE: Metadata Augmented Private Language Evolution
par: Chien, Eli, et autres
Publié: (2026)

Data Poisoning to Fake a Nash Equilibrium in Markov Games
par: Wu, Young, et autres
Publié: (2023)

Practical Feasibility of Gradient Inversion Attacks in Federated Learning
par: Valadi, Viktor, et autres
Publié: (2025)

Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
par: Wu, Zhaomin, et autres
Publié: (2024)

FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning
par: Zheng, Tianhang, et autres
Publié: (2024)

Tackling the Local Bias in Federated Graph Learning
par: Zhang, Binchi, et autres
Publié: (2021)

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
par: Scheliga, Daniel, et autres
Publié: (2023)

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
par: Xu, Yukai, et autres
Publié: (2024)

Can LLMs get help from other LLMs without revealing private information?
par: Hartmann, Florian, et autres
Publié: (2024)

Local Model Reconstruction Attacks in Federated Learning and their Uses
par: Driouich, Ilias, et autres
Publié: (2022)

Security Assessment of Hierarchical Federated Deep Learning
par: Alqattan, D, et autres
Publié: (2024)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
par: Zheng, Shuyuan, et autres
Publié: (2025)

Hush! Protecting Secrets During Model Training: An Indistinguishability Approach
par: Ganesh, Arun, et autres
Publié: (2025)

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
par: Lee, Joohyung, et autres
Publié: (2023)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
par: Dong, Wenhan, et autres
Publié: (2024)

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning
par: Zhang, Xiaojin, et autres
Publié: (2024)

No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
par: Diana, Francesco, et autres
Publié: (2026)

Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
par: Németh, Gergely Dániel, et autres
Publié: (2023)

Can Large Language Models Really Recognize Your Name?
par: Pham, Dzung, et autres
Publié: (2025)

Toward provably private analytics and insights into GenAI use
par: Cheu, Albert, et autres
Publié: (2025)

From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
par: Quy, Vu Khanh, et autres
Publié: (2024)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
par: Diana, Francesco, et autres
Publié: (2025)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
par: Xu, Zhangchen, et autres
Publié: (2024)

Data Valuation and Detections in Federated Learning
par: Li, Wenqian, et autres
Publié: (2023)

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
par: Dvijotham, Krishnamurthy, et autres
Publié: (2024)

Optimal Attack and Defense for Reinforcement Learning
par: McMahan, Jeremy, et autres
Publié: (2023)

Personalized Federated Learning Techniques: Empirical Analysis
par: Khan, Azal Ahmad, et autres
Publié: (2024)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
par: Li, Yuecheng, et autres
Publié: (2024)

Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
par: Liu, Ken Ziyu, et autres
Publié: (2025)

Privacy-Preserving in Blockchain-based Federated Learning Systems
par: M., Sameera K., et autres
Publié: (2024)

Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
par: Zuo, Shiyuan, et autres
Publié: (2024)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
par: Yang, Jirui, et autres
Publié: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
par: Ghosh, Bishwamittra, et autres
Publié: (2024)

Structure-Aware Distributed Backdoor Attacks in Federated Learning
par: Jian, Wang, et autres
Publié: (2026)