Designing a robust federated learning approach in a heterogeneous environment

Proposed by Cagri Ozcinar – cagriozcinar at gmail.com

Abstract: Federated learning has received attention for its efficiency and privacy benefits, in settings where data is distributed among devices. Although federated learning shows significant promise as a key approach when data cannot be shared or centralized, current incarnations show limited privacy properties and have shortcomings when applied to common real-world scenarios.
Devices frequently generate and collect data in a non-identically distributed manner across the network. For example, mobile phone users may have varied use of language in the context of the next word prediction task. Moreover, the number of data points across devices may vary
significantly, and there may be an underlying structure present that captures the relationship between devices and their associated distributions.

In this dissertation, we would like to create a more robust, efficient, and privacy guaranteed framework using federated learning.

References:

• https://www.forbes.com/sites/marymeehan/2019/11/26/data-privacy-will-be-the-most-important-issue-in-the-next-decade/#3211e2821882
• https://www.nature.com/articles/s42256-020-0186-1
• https://federated.withgoogle.com/#learn
• https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9146141
• https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

Requirement: 

• Basic understanding of Deep-learning,
• Strong Python programming skills with knowledge of PyTorch/TensorFlow tools.
• Ideal candidates should have interest in deep learning in general, and must have the ability to follow new research trends and learn new tools.