Secure and scalable robust federated learning

Liu, Andrew

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

Description

Title

Secure and scalable robust federated learning

Author(s)

Liu, Andrew

Issue Date

2022-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Khurana, Dakshita

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

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

Abstract

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.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Andrew Liu

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