A Communication Efficient Vertical Federated Learning Framework

12/24/2019
by   Yang Liu, et al.
23

One critical challenge for applying today's Artificial Intelligence (AI) technologies to real-world applications is the common existence of data silos across different organizations. Due to legal, privacy and other practical constraints, data from different organizations cannot be easily integrated. Federated learning (FL), especially the vertical FL (VFL), allows multiple parties having different sets of attributes about the same user collaboratively build models while preserving user privacy. However, communication overhead is a principal bottleneck since the existing VFL protocols require per-iteration communications among all parties. In this paper, we propose the Federated Stochastic Block Coordinate Descent (FedBCD) to effectively reduce the communication rounds for VFL. We show that when the batch size, sample size, and the local iterations are selected appropriately, the algorithm requires O(√(T)) communication rounds to achieve O(1/√(T)) accuracy. Finally, we demonstrate the performance of FedBCD on several models and datasets, and on a large-scale industrial platform for VFL.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/01/2019

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

Data privacy and security becomes a major concern in building machine le...
research
05/18/2023

Efficient Vertical Federated Learning with Secure Aggregation

The majority of work in privacy-preserving federated learning (FL) has b...
research
07/29/2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates

Vertical federated learning (VFL) is an emerging paradigm that allows di...
research
02/22/2023

Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

Artificial intelligence (AI)-empowered industrial fault diagnostics is i...
research
03/05/2021

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

Federated learning (FL) has been proposed to allow collaborative trainin...
research
12/15/2021

Data Valuation for Vertical Federated Learning: An Information-Theoretic Approach

Federated learning (FL) is a promising machine learning paradigm that en...
research
05/18/2021

DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities

We have entered the era of big data, and it is considered to be the "fue...

Please sign up or login with your details

Forgot password? Click here to reset