Ph.D project description
Project title: Federated Learning for Online Collaborative Knowledge and Decision-making
Main supervisor: Lukas Esterle
Co-supervisor(s): Alexandros Iosifidis
Project period: 01/10/2021 to 30/09/2024
Increases in computational power and storage within devices in Internet-of-Things(IoT) settings allows devices to utilize Machine Learning(ML). The advanced models from ML allows for more accurate decision-making, classification and inference. However, training these models is resource expensive and time consuming. An approach to train the model on is Federated Learning(FL). FL allows the training of a single model by multiple devices in a distributed fashion using only local information. Unfortunately IoT devices often require individual and specialized models due to their location and inherently different perceptions of the world. This project will examine object tracking and action recognition, where the individual devices are subject to changes in viewing angles, background and brightness.
The project will examine if it is possible to retain and combine the aspects of a globally trained model with the individual specialised models. Using this approach we can benefit from both the global and specialised models, exploring if inference is improved by combining the specialized model with the globally trained model. Furthermore I will explore the capability of individual devices to specialise their learning while enabling them to collaborate on different tasks.
