View Proposal

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Proposer

Chengjia Wang

Title

Trustworthy serverless machine learning on heterogeneous and distributed data and devices

Goal

Build up a distributed machine learning system for collaborative decision making without data exchange

Description

Deep convoloutional networks have been widely deployed in modern cyber-physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this project, a heterogeneous brain storming (HBS) method is implemented and developed for object recognition tasks in real-world Internet of Things (IoT) scenarios. This method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new “brain storming” mechanism and optimizable temperature parameters.

Resources

https://ieeexplore.ieee.org/abstract/document/9134802

Background

Url

External Link

Difficulty Level

High

Ethical Approval

None

Number Of Students

0

Supervisor

Chengjia Wang

Keywords

machine learning, deep learning, federated learning, ai, computer vision, nlp, multi-modality, iot

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Bachelor of Science in Information Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics