DeepAI AI Chat
Log In Sign Up

IFedAvg: Interpretable Data-Interoperability for Federated Learning

07/14/2021
by   David Roschewitz, et al.
EPFL
ETH Zurich
0

Recently, the ever-growing demand for privacy-oriented machine learning has motivated researchers to develop federated and decentralized learning techniques, allowing individual clients to train models collaboratively without disclosing their private datasets. However, widespread adoption has been limited in domains relying on high levels of user trust, where assessment of data compatibility is essential. In this work, we define and address low interoperability induced by underlying client data inconsistencies in federated learning for tabular data. The proposed method, iFedAvg, builds on federated averaging adding local element-wise affine layers to allow for a personalized and granular understanding of the collaborative learning process. Thus, enabling the detection of outlier datasets in the federation and also learning the compensation for local data distribution shifts without sharing any original data. We evaluate iFedAvg using several public benchmarks and a previously unstudied collection of real-world datasets from the 2014 - 2016 West African Ebola epidemic, jointly forming the largest such dataset in the world. In all evaluations, iFedAvg achieves competitive average performance with negligible overhead. It additionally shows substantial improvement on outlier clients, highlighting increased robustness to individual dataset shifts. Most importantly, our method provides valuable client-specific insights at a fine-grained level to guide interoperable federated learning.

READ FULL TEXT
03/08/2021

Personalized Federated Learning using Hypernetworks

Personalized federated learning is tasked with training machine learning...
12/15/2020

Personalized Federated Learning with First Order Model Optimization

While federated learning traditionally aims to train a single global mod...
10/13/2021

WAFFLE: Weighted Averaging for Personalized Federated Learning

In collaborative or federated learning, model personalization can be a v...
12/01/2021

Federated Learning with Adaptive Batchnorm for Personalized Healthcare

There is a growing interest in applying machine learning techniques for ...
01/22/2020

Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients

Federated learning is an effective way of training a machine learning mo...
05/03/2022

FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning

Robustness is becoming another important challenge of federated learning...
06/23/2022

EFFGAN: Ensembles of fine-tuned federated GANs

Generative adversarial networks have proven to be a powerful tool for le...

Code Repositories

ifedavg

iFedAvg - Interpretable Data-Interoperability for Federated Learning


view repo