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Found 5 papers, 1 papers with code
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.
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-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.
