Voltran: Unlocking Trust and Confidentiality in Decentralized Federated Learning Aggregation

TL;DR

Voltran is a hybrid platform combining TEE and blockchain to enhance trust, confidentiality, and scalability in decentralized federated learning, addressing limitations of existing blockchain-based FL schemes.

Contribution

It introduces a TEE-based off-chain aggregation method with blockchain verification and a multi-SGX parallel strategy for scalable, secure federated learning.

Findings

Minimal overhead with strong trust and confidentiality guarantees

Significant speed-up over existing ciphertext aggregation schemes

Effective scalability across multiple FL scenarios

Abstract

The decentralized Federated Learning (FL) paradigm built upon blockchain architectures leverages distributed node clusters to replace the single server for executing FL model aggregation. This paradigm tackles the vulnerability of the centralized malicious server in vanilla FL and inherits the trustfulness and robustness offered by blockchain. However, existing blockchain-enabled schemes face challenges related to inadequate confidentiality on models and limited computational resources of blockchains to perform large-scale FL computations. In this paper, we present Voltran, an innovative hybrid platform designed to achieve trust, confidentiality, and robustness for FL based on the combination of the Trusted Execution Environment (TEE) and blockchain technology. We offload the FL aggregation computation into TEE to provide an isolated, trusted and customizable off-chain execution, and then guarantee the authenticity and verifiability of aggregation results on the blockchain. Moreover, we provide strong scalability on multiple FL scenarios by introducing a multi-SGX parallel execution strategy to amortize the large-scale FL workload. We implement a prototype of Voltran and conduct a comprehensive performance evaluation. Extensive experimental results demonstrate that Voltran incurs minimal additional overhead while guaranteeing trust, confidentiality, and authenticity, and it significantly brings a significant speed-up compared to state-of-the-art ciphertext aggregation schemes.