Federated learning is a paradigm breakthrough shift in AI for data privacy preservation. Unlike conventional artificial intelligence (AI) techniques, federated learning does not require the users to share the data, but only the AI model parameters. As such, federated learning has found notable successes in numerous applications (e.g., Google keyboard, localization, security, and data sharing) and various disciplines (e.g., finance, healthcare, and mobile networking). However, the implementation of federated learning algorithms requires considering both computing and communication aspects, in which the communication between the learning server and FL clients is over wireless networks (e.g., beyond 5G and 6G). I can supervise the following topics of wireless federated learning.
Federated learning over cybersecurity
Federated learning aims to protect data privacy and somehow enhance user security. However, cyberattacks are frequently happening in various services and applications that are deployed in real-time and over wireless communication systems. This topic aims to develop new federated learning methods to enhance data privacy and improve the performance of cyber systems.
The primary objective of this topic is to understand and explore how federated learning can improve the performance of cybersecurity systems. The key goals are as follows:
- Understand the fundamentals of cybersecurity and explore important research aspects of cybersecurity in protecting wireless networks and the Internet-of-Things.
- Understand the concept of distributed learning and federated learning.
- Investigate a new deep federated learning method for IoT cyberattack classification.
Communication-efficient federated learning
Communication efficiency is an utmost important issue in distributed machine learning approaches like federated learning. There are many ways to design communication-efficient federated learning, such as AI model compression and communication resource management. This project aims to design a new communication-efficient approach to federated learning.
The primary objective of this topic is to understand the communication efficiency issue in federated learning. The key goals are as follows:
- Understand the concept of distributed learning and federated learning.
- Understand the communication efficiency issue in federated learning.
- Apply engineering mathematics and convex optimization to analyze the model convergence and then optimize the considered federated learning system.
Deep reinforcement learning for optimizing federated learning networks
The federated learning algorithms are typically employed over wireless communications, where the channels are highly dynamic and varying. As such, deep reinforcement learning (DRL) is a suitable tool for decision-making of federated learning networks. This topic aims to leverage state-of-the-art DRL techniques or develop a new DRL approach to solve resource allocation problems in federated learning systems.
The primary objective of this topic is to understand how to apply DRL techniques for optimizing federated learning systems. The key goals are as follows:
- Understand the usage of classic and state-of-the-art DRL techniques for decision-making in wireless communications and networks.
- Understand the concept of distributed learning and federated learning.
- Develop a new DRL approach to solve resource allocation problems in federated learning systems.
Graph neural networks for federated learning
Graph neural network (GNN) is a kind of deep neural network for processing data that can be represented as a graph. The applications of GNNs have found notable success in various fields, including mobile communications and networking. Following this topic, the students can understand federated learning and graph neural networks and investigate the usage of graph neural networks to enhance the performance of conventional model-based algorithms in federated learning.
The primary objective of this topic is to understand and explore how federated learning can improve the performance of cybersecurity systems. The key goals are as follows:
- Understand the fundamentals of GNNs and explore the usage of GNNs in developing advanced algorithms for data processing.
- Understand the concept of distributed learning and federated learning.
- Develop a new GNN-based learning algorithm to solve a resource allocation problem in federated learning.
In addition to the topics mentioned above, I am happy to discuss any project ideas (e.g., federated unlearning and decentralized learning) about this exciting research area. More information: https://www.scss.tcd.ie/viet.pham