Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

We introduce a new class of attacks on machine learning models. We show that an
adversary who can poison a training dataset can cause models trained on this dataset to …

In federated learning (FL), data does not leave personal devices when they are jointly
training a machine learning model. Instead, these devices share gradients, parameters, or …

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 …

While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a lack of work considering DP and its implications for cross …

Federated learning was introduced to enable machine learning over large decentralized
datasets while promising privacy by eliminating the need for data sharing. Despite this, prior …

Secure aggregation promises a heightened level of privacy in federated learning,
maintaining that a server only has access to a decrypted aggregate update. Within this …

Federated learning (FL) has gained widespread popularity in a variety of industries due to its
ability to locally train models on devices while preserving privacy. However, FL systems are …

Memorization of training data is an active research area, yet our understanding of the inner
workings of neural networks is still in its infancy. Recently, Haim et al. 2022 proposed a …

Federated learning (FL) aims to perform privacy-preserving machine learning on distributed
data held by multiple data owners. To this end, FL requires the data owners to perform …