Data Heterogeneity in Federated Learning - Revision history

Slawek at 16:52, 17 September 2022

2022-09-17T16:52:30Z

Slawek at 15:48, 20 September 2021

2021-09-20T15:48:27Z

Amira at 10:16, 8 October 2020

2020-10-08T10:16:00Z

Amira at 10:14, 8 October 2020

2020-10-08T10:14:31Z

Amira: Created page with "{{StudentProjectTemplate |Summary=Addressing the challenges of data imbalance in Federated Learning |Keywords=non-IID data, Federated Learning |Supervisor=Amira Soliman, Slawo..."

2020-10-08T09:41:02Z

Created page with "{{StudentProjectTemplate |Summary=Addressing the challenges of data imbalance in Federated Learning |Keywords=non-IID data, Federated Learning |Supervisor=Amira Soliman, Slawo..."

New page

{{StudentProjectTemplate
|Summary=Addressing the challenges of data imbalance in Federated Learning
|Keywords=non-IID data, Federated Learning
|Supervisor=Amira Soliman, Slawomir Nowaczyk
}}
Federated Learning (FL) has been introduced as an alternative distributed and privacy-friendly learning approach. FL allows users to train models locally on their devices using their sensitive data, and communicate intermediate model updates to a central server without the need to centrally store the data. The principal advantage of FL is the decoupling of global model training from the need for direct access to the raw data. Accordingly, FL offers a solution to learn from private personal data such as biometrics, text input, and location coordinates where models can be trained for many services in a privacy-preserving manner.

A common assumption in FL is that each node has an unbiased sample of the complete data. In reality, though, the models created by different users can often be quite different, as the data on each device can originate from different phenomena. For example, two randomly picked users are likely to compute very different updates to a typing prediction model. This leads to a situation that is challenging from the statistical standpoint, and most existing methods make strong assumptions for how skewed the data distributions are. However, in open and decentralized environments, imbalanced data as well as missing classes are common, and it is imperative that FL methods can deal with them. A lot of work has been done for solving class imbalance and missing classes in a centralized setting, but it is more challenging to provide practical and privacy-preserving learning methods for imbalanced data in FL environments. The objective of this thesis is to create an FL algorithm that is capable of handling the data imbalance among participating devices and propose a solution to enhance model training under this imbalance.

@@ Line 2: / Line 2: @@
 |Summary=Addressing the challenges of data imbalance in Federated Learning
 |Keywords=non-IID data, Federated Learning
-|TimeFrame=Fall 2021
+|TimeFrame=Fall 2022
 |References=Advances and Open Problems in Federated Learning:
 https://hal.inria.fr/hal-02406503/document

@@ Line 2: / Line 2: @@
 |Summary=Addressing the challenges of data imbalance in Federated Learning
 |Keywords=non-IID data, Federated Learning
 |References=Advances and Open Problems in Federated Learning:
 https://hal.inria.fr/hal-02406503/document
 FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf
 |Supervisor=Amira Soliman, Slawomir Nowaczyk
 |Level=Master

@@ Line 3: / Line 3: @@
 |Keywords=non-IID data, Federated Learning
 |Supervisor=Amira Soliman, Slawomir Nowaczyk
 }}
 Federated Learning (FL) has been introduced as an alternative distributed and privacy-friendly learning approach. FL allows users to train models locally on their devices using their sensitive data, and communicate intermediate model updates to a central server without the need to centrally store the data. The principal advantage of FL is the decoupling of global model training from the need for direct access to the raw data. Accordingly, FL offers a solution to learn from private personal data such as biometrics, text input, and location coordinates where models can be trained for many services in a privacy-preserving manner.
 A common assumption in FL is that each node has an unbiased sample of the complete data. In reality, though, the models created by different users can often be quite different, as the data on each device can originate from different phenomena. For example, two randomly picked users are likely to compute very different updates to a typing prediction model. This leads to a situation that is challenging from the statistical standpoint, and most existing methods make strong assumptions for how skewed the data distributions are. However, in open and decentralized environments, imbalanced data as well as missing classes are common, and it is imperative that FL methods can deal with them. A lot of work has been done for solving class imbalance and missing classes in a centralized setting, but it is more challenging to provide practical and privacy-preserving learning methods for imbalanced data in FL environments. The objective of this thesis is to create an FL algorithm that is capable of handling the data imbalance among participating devices and propose a solution to enhance model training under this imbalance.