DeepAI AI Chat
Log In Sign Up

Variational Federated Multi-Task Learning

06/14/2019
by   Luca Corinzia, et al.
ETH Zurich
0

In classical federated learning a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-task learning framework, to handle real-world federated datasets that typically show strong non-IID data distributions among devices. Even though federated multi-task learning has been shown to be an effective paradigm for real world datasets, it has been applied only to convex models. In this work we introduce VIRTUAL, an algorithm for federated multi-task learning with non-convex models. In VIRTUAL the federated network of the server and the clients is treated as a star-shaped Bayesian network, and learning is performed on the network using approximated variational inference. We show that this method is effective on real-world federated datasets, outperforming the current state-of-the-art for federated learning.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/30/2017

Federated Multi-Task Learning

Federated learning poses new statistical and systems challenges in train...
11/12/2020

Fed-Focal Loss for imbalanced data classification in Federated Learning

The Federated Learning setting has a central server coordinating the tra...
04/19/2020

Data Poisoning Attacks on Federated Machine Learning

Federated machine learning which enables resource constrained node devic...
02/14/2021

FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization

Federated multi-task learning (FMTL) has emerged as a natural choice to ...
11/14/2020

A Theoretical Perspective on Differentially Private Federated Multi-task Learning

In the era of big data, the need to expand the amount of data through da...
06/04/2021

SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks

Graph Neural Networks (GNNs) are the first choice methods for graph mach...
12/08/2020

Federated Multi-Task Learning for Competing Constraints

In addition to accuracy, fairness and robustness are two critical concer...

Code Repositories