Blockchain-Empowered Federated Learning

Supervisory Team: Leonardo Aniello, Han Wu

Project description:

Blockchain and Federated Learning (FL) are two emerging technologies that, when combined, offer a powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy. Blockchain, on the other hand, provides a secure, tamper-resistant ledger to manage decentralised transactions. By integrating these two technologies, we can improve the transparency, security, and incentivisation of participants in FL ecosystems.

This PhD project aims to address one or more of several critical research challenges at the intersection of blockchain and FL. One major challenge is designing fair reward mechanisms that can incentivise participants to contribute to the training process. Entities should be fairly compensated based on their contributions, which requires developing methods to assess the quality and significance of their local models.

A related challenge is evaluating the contributions of participating entities. To ensure a robust and efficient global model, it is essential to quantify how much each client’s update improves the overall model. This will allow for more equitable reward allocation and the ability to discard unreliable clients whose updates may degrade the model’s performance.

Another key area of focus is integrating blockchain consensus protocols with FL protocols. The goal is to streamline communication and computation by reducing overlaps and redundancies. This can help achieve better efficiency in both training time and resource utilisation, making the FL process more scalable.

Finally, the project will explore methods to enhance security against malicious entities, particularly those attempting data poisoning attacks. Blockchain’s immutability and transparency, combined with novel detection algorithms, could provide a robust defence mechanism to maintain the integrity of the FL process.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 31 August 2025.

Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding:

We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. 

For more information please visit PhD Scholarships Doctoral College University of Southampton 

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online by clicking the ‘Apply’ button, above. 

Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Computer Science (Full time)”.

In Section 2 of the application form you should insert the name of the supervisor Leonardo Aniello

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: [email protected]

The School of Electronics & Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.