Flatee: Federated Learning Across Trusted Execution Environments

TL;DR

Flatee introduces an efficient federated learning framework leveraging Trusted Execution Environments to enhance privacy and reduce training and communication times, addressing practical deployment challenges.

Contribution

The paper presents Flatee, a novel federated learning framework that utilizes TEEs for secure, efficient, and scalable model training across multiple parties.

Findings

Significantly reduces training time and communication overhead.

Provides a privacy-preserving solution using TEEs.

Handles malicious parties with a preliminary approach.

Abstract

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).