Capc learning: Confidential and private collaborative learning
Machine learning benefits from large training datasets, which may not always be possible to
collect by any single entity, especially when using privacy-sensitive data. In many contexts …
collect by any single entity, especially when using privacy-sensitive data. In many contexts …
Protection against reconstruction and its applications in private federated learning
In large-scale statistical learning, data collection and model fitting are moving increasingly
toward peripheral devices---phones, watches, fitness trackers---away from centralized data …
toward peripheral devices---phones, watches, fitness trackers---away from centralized data …
Scalable privacy-preserving distributed learning
D Froelicher, JR Troncoso-Pastoriza, A Pyrgelis… - arXiv preprint arXiv …, 2020 - arxiv.org
In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …
evaluation of machine-learning models by analyzing it in the widespread MapReduce …
[PDF][PDF] A federated learning framework for privacy-preserving and parallel training
Deep learning has achieved great success in many artificial intelligence applications such
as healthcare systems, recommendation systems, and network security. However, the …
as healthcare systems, recommendation systems, and network security. However, the …
Compression boosts differentially private federated learning
Federated Learning allows distributed entities to train a common model collaboratively
without sharing their own data. Although it prevents data collection and aggregation by …
without sharing their own data. Although it prevents data collection and aggregation by …
Flame: Differentially private federated learning in the shuffle model
Federated Learning (FL) is a promising machine learning paradigm that enables the
analyzer to train a model without collecting users' raw data. To ensure users' privacy …
analyzer to train a model without collecting users' raw data. To ensure users' privacy …
Private federated learning with autotuned compression
E Ullah, CA Choquette-Choo… - … on Machine Learning, 2023 - proceedings.mlr.press
We propose new techniques for reducing communication in private federated learning
without the need for setting or tuning compression rates. Our on-the-fly methods …
without the need for setting or tuning compression rates. Our on-the-fly methods …
[HTML][HTML] Differentially private knowledge transfer for federated learning
Extracting useful knowledge from big data is important for machine learning. When data is
privacy-sensitive and cannot be directly collected, federated learning is a promising option …
privacy-sensitive and cannot be directly collected, federated learning is a promising option …
Privcoll: Practical privacy-preserving collaborative machine learning
Collaborative learning enables two or more participants, each with their own training
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …
LDP-FL: Practical private aggregation in federated learning with local differential privacy
Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …
concerns in many areas. Federated learning is a popular approach for privacy protection …