Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

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 attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

In the robust secure aggregation problem, a server wishes to learn and only learn the sum of
the inputs of a number of users while some users may drop out (ie, may not respond). The …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

While federated learning (FL) systems often utilize quantization to battle communication and
computational bottlenecks they have heretofore been limited to deploying fixed-precision …

We present two novel federated learning (FL) schemes that mitigate the effect of straggling
devices by introducing redundancy on the devices' data across the network. Compared to …

Security and privacy are important concerns in machine learning. End user devices often
contain a wealth of data and this information is sensitive and should not be shared with …