Blockchain-based Secure Client Selection in Federated Learning

TL;DR

This paper introduces a blockchain-based client selection protocol for federated learning that enhances privacy by preventing server manipulation, ensuring secure and verifiable client selection.

Contribution

It proposes a novel blockchain-enabled protocol for verifiable client selection in federated learning, addressing privacy vulnerabilities in existing secure aggregation methods.

Findings

The protocol prevents server manipulation of client selection.

Security proofs confirm robustness against targeted attacks.

Experiments demonstrate practical feasibility on Ethereum-like blockchain.

Abstract

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.