287 papers with code • 0 benchmarks • 7 datasets
Federated Learning is a framework to train a centralized model for a task where the data is de-centralized across different devices/ silos.
This helps preserve privacy of data on various devices as only the weight updates are shared with the centralized model so the data can remain on each device and we can still train a model using that data.
The broad application of artificial intelligence techniques ranging from self-driving vehicles to advanced medical diagnostics afford many benefits.
However, existing MIAs ignore the source of a training member, i. e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients.
In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings.
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally.
Federated learning aims to protect users' privacy while performing data analysis from different participants.
A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets.
Federated Learning is an algorithm suited for training models on decentralized data, but the requirement of a central "server" node is a bottleneck.