Asynchronous Secure Federated Learning with Byzantine aggregators

TL;DR

This paper presents a novel asynchronous federated learning framework that ensures privacy and robustness against malicious aggregators by using replication, secure masking, differential privacy, and an inclusion mechanism to balance client participation.

Contribution

It introduces a new privacy-preserving protocol for asynchronous federated learning with Byzantine aggregators, avoiding consensus and enhancing availability.

Findings

Maintains performance comparable to state-of-the-art methods.

Ensures privacy and robustness against malicious aggregators.

Provides a balanced participation mechanism for clients.

Abstract

Privacy-preserving federated averaging is a central approach for protecting client privacy in federated learning. In this paper, we study this problem in an asynchronous communications setting with malicious aggregators. We propose a new solution to provide federated averaging in this model while protecting the client's data privacy through secure aggregation and differential privacy. Our solution maintains the same performance as the state of the art across all metrics. The main contributions of this paper are threefold. First, unlike existing single- or multi-server solutions, we consider malicious aggregation servers that may manipulate the model to leak clients' data or halt computation. To tolerate this threat, we replicate the aggregators, allowing a fraction of them to be corrupted. Second, we propose a new privacy preservation protocol for protocols in asynchronous communication models with Byzantine aggregators. In this protocol, clients mask their values and add Gaussian noise to their models. In contrast with previous works, we use the replicated servers to unmask the models, while ensuring the liveness of training even if aggregators misbehave. Third, the asynchronous communication model introduces new challenges not present in existing approaches. In such a setting, faster clients may contribute more frequently, potentially reducing their privacy and biasing the training. To address this, we introduce an inclusion mechanism that ensures uniform client participation and balanced privacy budgets. Interestingly, the solution presented in this paper does not rely on agreement between aggregators. Thus, we circumvent the known impossibility of consensus in asynchronous settings where processes might crash. Additionally, this feature increases availability, as a consensus-based algorithm only progresses in periods of low latency.