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
Language
eng
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.
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