Search Results for author:
Found 9 papers, 2 papers with code
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models.
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability.
A Sample-Based Algorithm for Approximately Testing $r$-Robustness of a Digraph
In this paper, we propose a sample-based algorithm to approximately test $r$-robustness of a digraph with $n$ vertices and $m$ edges.
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Our work provides the first theoretical analysis of the effect message compression has on distributed training over vertically partitioned data.
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients.
Multi-Tier Federated Learning for Vertically Partitioned Data
Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients.
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks
We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers.
Multi-Level Local SGD for Heterogeneous Hierarchical Networks
In our algorithm, sub-networks execute a distributed SGD algorithm, using a hub-and-spoke paradigm, and the hubs periodically average their models with neighboring hubs.
EdgeBench: Benchmarking Edge Computing Platforms
The emerging trend of edge computing has led several cloud providers to release their own platforms for performing computation at the 'edge' of the network.
Networking and Internet Architecture Distributed, Parallel, and Cluster Computing
