Evaluating differential privacy on language processing federated learning

Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they are computationally expensive [14]. This paper evaluates a version...

Performance comparison of different federated learning aggregation algorithms: How does the performance of different federated learning aggregation algorithms compare to each other?

Federated learning enables the construction of machine learning models, while adhering to privacy constraints and without sharing data between different devices. It is achieved by creating a machine learning model on each device that contains data, and then combining these models through an aggregation algorithm without sharing the data....

Improving privacy of Federated Learning Generative Adversarial Networks using Intel SGX

Federated learning (FL), although a major privacy improvement over centralized learning, is still vulnerable to privacy leaks. The research presented in this paper provides an analysis of the threats to FL Generative Adversarial Networks. Furthermore, an implementation is provided to better protect the data of the participants with Trusted...

Privacy Preserving Vertical Federated Learning: A Literature Study

Federated learning (FL) is a new paradigm that allows several parties to train a model together without sharing their proprietary data. This paper investigates vertical federated learning, which addresses scenarios in which collaborating organizations own data from the same set of users but with differing features. The survey provides an...

Horizontal Federated Learning Frameworks: A Literature Study

Federated Learning (FL)[1] is a type of distributed machine learning that allows the owners of the training data to preserve their privacy while still be- ing able to collectively train a model. FL is a new area in research and several chal- lenges reagarding privacy and communication cost still need to be overcome. Gradient leakage[1], for...

A Survey of Two Open Problems of Privacy-Preserving Federated Learning: Vertically Partitioned Data and Verifiability

Federated learning is a machine learning technique proposed by Google AI in 2016, as a solution to the GDPR regulations that made the classical Centralized Training, not only unfeasible, but also illegal, in some cases. In spite of its potential, FL has not gained much trust in the community, especially because of its susceptibility to data...

An overview of hybrid approaches in Horizontal Federated Learning

Federated Learning starts to give a new perspective regarding the applicability of machine learning in real-life scenarios. Its main goal is to train the model while keeping the participants' data in their devices, thus guaranteeing the privacy of their data. One of the main architectures is the Horizontal Federated Learning, which is the most...