Personalized federated learning with differential privacy
To provide intelligent and personalized services on smart devices, machine learning
techniques have been widely used to learn from data, identify patterns, and make automated …
techniques have been widely used to learn from data, identify patterns, and make automated …
Hybrid differential privacy based federated learning for Internet of Things
W Liu, J Cheng, X Wang, X Lu, J Yin - Journal of Systems Architecture, 2022 - Elsevier
Wireless sensor networks have been widely used to achieve fine-grained information
collection. However, numerous data acquisition and processing of sensors bring some …
collection. However, numerous data acquisition and processing of sensors bring some …
Preserving user privacy for machine learning: Local differential privacy or federated machine learning?
The growing number of mobile and IoT devices has nourished many intelligent applications.
In order to produce high-quality machine learning models, they constantly access and …
In order to produce high-quality machine learning models, they constantly access and …
Dynamic personalized federated learning with adaptive differential privacy
Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …
solution to address non-IID distribution of data and privacy leakage risks. However, current …
Local differential privacy-based federated learning for internet of things
The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …
User-level privacy-preserving federated learning: Analysis and performance optimization
Federated learning (FL), as a type of collaborative machine learning framework, is capable
of preserving private data from mobile terminals (MTs) while training the data into useful …
of preserving private data from mobile terminals (MTs) while training the data into useful …
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 …
Privacy, accuracy, and model fairness trade-offs in federated learning
X Gu, Z Tianqing, J Li, T Zhang, W Ren, KKR Choo - Computers & Security, 2022 - Elsevier
As applications of machine learning become increasingly widespread, the need to ensure
model accuracy and fairness while protecting the privacy of user data becomes more …
model accuracy and fairness while protecting the privacy of user data becomes more …
FedMEC: improving efficiency of differentially private federated learning via mobile edge computing
Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …
privacy-preserving machine learning services. It enables the deep learning applications on …
Differentially private asynchronous federated learning for mobile edge computing in urban informatics
Y Lu, X Huang, Y Dai, S Maharjan… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Driven by technologies such as mobile edge computing and 5G, recent years have
witnessed the rapid development of urban informatics, where a large amount of data is …
witnessed the rapid development of urban informatics, where a large amount of data is …