Federated Learning and Differential Privacy: Software tools analysis, the Sherpa. ai FL framework and methodological guidelines for preserving data privacy
The high demand of artificial intelligence services at the edges that also preserve data
privacy has pushed the research on novel machine learning paradigms that fit these …
privacy has pushed the research on novel machine learning paradigms that fit these …
From distributed machine learning to federated learning: In the view of data privacy and security
Federated learning is an improved version of distributed machine learning that further
offloads operations which would usually be performed by a central server. The server …
offloads operations which would usually be performed by a central server. The server …
Federated learning as a privacy solution-an overview
Abstract The Fourth Industrial Revolution suggests smart and automated industrial solutions
by incorporating Artificial Intelligence into it. Today, the world of technology is highly …
by incorporating Artificial Intelligence into it. Today, the world of technology is highly …
Advancements of federated learning towards privacy preservation: from federated learning to split learning
In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL)
recently attracted much attention due to its applications in health, finance, and the latest …
recently attracted much attention due to its applications in health, finance, and the latest …
[HTML][HTML] Privacy preservation in federated learning: An insightful survey from the GDPR perspective
In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …
services, ensuring data privacy and security have become a critical obligation. ML-based …
Scalable federated machine learning with fedn
M Ekmefjord, A Ait-Mlouk, S Alawadi… - 2022 22nd IEEE …, 2022 - ieeexplore.ieee.org
Federated machine learning promises to overcome the input privacy challenge in machine
learning. By iteratively updating a model on private clients and aggregating these local …
learning. By iteratively updating a model on private clients and aggregating these local …
Differential privacy meets federated learning under communication constraints
The performance of federated learning systems is bottlenecked by communication costs and
training variance. The communication overhead problem is usually addressed by three …
training variance. The communication overhead problem is usually addressed by three …
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 …
Exploring privacy measurement in federated learning
Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables
distributed training of AI models without data sharing, thereby promoting privacy by design …
distributed training of AI models without data sharing, thereby promoting privacy by design …
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