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 …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

Language model training in distributed settings is limited by the communication cost of
gradient exchanges. In this short note, we extend recent work from Malladi et al.(2023) …

Network intrusions through jamming and spoofing attacks have become increasingly
prevalent. The ability to detect such threats at early stages is necessary for preventing a …

Secure neural network inference has been a promising solution to private Deep-Learning-as-
a-Service, which enables the service provider and user to execute neural network inference …

Cross-silo federated learning (FL) enables multiple clients to collaboratively train a machine
learning model without sharing training data, but privacy in FL remains a major challenge …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

With the increasing number of big data applications, large amounts of valuable data are
distributed in different organizations or regions. Federated Learning (FL) enables …