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Found 8 papers, 4 papers with code
FedLPA: Personalized One-shot Federated Learning with Layer-Wise Posterior Aggregation
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users.
PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission.
On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections
This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks.
Challenges of Privacy-Preserving Machine Learning in IoT
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.
