Privacy-Preserving Federated Learning Framework via Confidential Containers & KubeStellar

Posted: Aug. 2024 - Present
Abstract

Developing a private computation space evaluating ML model for Digital Agriculture via KubeStellar & Secure Enclaves

Privacy-Preserving Federated Learning Framework via Confidential Containers & KubeStellar

Introduction

Responsibility

  • Designing a federated learning pipeline that enforces data confidentiality and model integrity
  • Integrating differential privacy and remote attestation to secure federated model training and evaluation