In a vertical federated learning (VFL) system consisting of a central se...
In a federated learning (FL) system, distributed clients upload their lo...
In this work, we propose a task called "Scene Style Text Editing (SSTE)"...
In many distributed learning setups such as federated learning (FL), cli...
The transferability of adversarial examples is a crucial aspect of evalu...
In this paper, we consider a hierarchical distributed multi-task learnin...
Federated learning (FL) has achieved great success as a privacy-preservi...
Federated learning (FL) strives to enable collaborative training of mach...
We consider a federated representation learning framework, where with th...
We consider a foundational unsupervised learning task of k-means data
cl...
In this paper, we study the two problems of Private and Secure Matrix
Mu...
We consider the problem of evaluating arbitrary multivariate polynomials...
We propose SwiftAgg+, a novel secure aggregation protocol for federated
...
We propose SwiftAgg, a novel secure aggregation protocol for federated
l...
Federated learning (FL) has attracted much attention as a privacy-preser...
We consider the problems of Private and Secure Matrix Multiplication (PS...
Secure model aggregation is a key component of federated learning (FL) t...
We propose OmniLytics, a blockchain-based secure data trading marketplac...
Most state machine replication protocols are either based on the 40-year...
In this paper, we propose coded Merkle tree (CMT), a novel hash accumula...
We introduce an information-theoretic framework, named Coded State Machi...
Distributed training of deep nets is an important technique to address s...
Data parallelism can boost the training speed of convolutional neural
ne...
Today's blockchains do not scale in a meaningful sense. As more nodes jo...
We consider the problem of training a least-squares regression model on ...
Communication overhead is one of the major performance bottlenecks in
la...
Modern learning algorithms use gradient descent updates to train inferen...