Robust and Actively Secure Serverless Collaborative Learning

TL;DR

This paper introduces a secure, peer-to-peer collaborative learning framework that ensures robustness and security against malicious servers and clients, enabling efficient training of large models in untrusted environments.

Contribution

A novel generic framework that transforms robust aggregation algorithms into a secure, peer-to-peer setting with malicious adversaries.

Findings

Framework is compatible with existing robust aggregation algorithms.

Achieves computational efficiency for large models with hundreds of peers.

Demonstrates security and robustness in standard datasets.

Abstract

Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively protect user data, they remain vulnerable to either the server, the clients, or both, deviating from the protocol. Indeed, because the protocol is asymmetric, a malicious server can abuse its power to reconstruct client data points. Conversely, malicious clients can corrupt learning with malicious updates. Thus, both clients and servers require a guarantee when the other cannot be trusted to fully cooperate. In this work, we propose a peer-to-peer (P2P) learning scheme that is secure against malicious servers and robust to malicious clients. Our core contribution is a generic framework that transforms any (compatible) algorithm for robust aggregation of model updates to the setting where servers and clients can act maliciously. Finally, we demonstrate the computational efficiency of our approach even with 1-million parameter models trained by 100s of peers on standard datasets.