Secure and scalable robust federated learning

With the rise of cloud computing in recent years, an increasing amount of data storage and computation are being offloaded from individual users’ computers to those of large corporations. These corporations benefit from merging their clients’ data, e.g. hospitals sharing client chest x-ray images to better detect and treat COVID-19. However, these institutions are restricted from sharing their client data due to both ethical and legal reasons. Federated learning is a technique that attempts to solve this problem by training a model across multiple edge devices while data stays on-device. This thesis focuses on the specific case of robust federated learning, where the central server coordinating the parties computes a robust aggregate of client updates to redistribute back as a global model. Cryptographic protocols such as secure multi-party computation and differential privacy are often added on top of federated learning to formally prove security. This work focuses on developing an efficient and private version of federated learning, when coordinate-wise median is used as a robust aggregator. A semi-honest protocol is developed for both median computation and approximate median computation. The convergence and robustness of this protocols is evaluated empirically.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Liu, Andrew

Contributors dc:contributor

• Khurana, Dakshita

• federated learning
• secure multi-party computation
• privacy preserving machine learning

Statement dc:rights

• Copyright 2022 Andrew Liu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115643

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115643