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.

通过这项工作，我们提出了一种对抗恶意服务器、抵御恶意客户端的点对点学习方案，核心贡献是一个通用框架，使得兼容的模型更新聚合算法能够在服务器和客户端恶意行为的环境下运行。此外，我们展示了我们的方法在由数百个节点对标准数据集训练的百万参数模型上的计算效率。

鲁棒性和实时安全的无服务器协作学习

Robust and Actively Secure Serverless Collaborative Learning

Federated learning is a collaborative method that aims to preserve data
privacy while creating AI models. Current approaches to federated learning tend
to rely heavily on secure aggregation protocols to preserve data privacy.
However, to some degree, such protocols assume that the entity orchestrating
the federated learning process (i.e., the server) is not fully malicious or
dishonest. We investigate vulnerabilities to secure aggregation that could
arise if the server is fully malicious and attempts to obtain access to
private, potentially sensitive data. Furthermore, we provide a method to
further defend against such a malicious server, and demonstrate effectiveness
against known attacks that reconstruct data in a federated learning setting.

本文研究了在联合学习中，如果服务端是恶意的但试图获得私人数据，会面临什么样的安全聚合漏洞，并提供了一种进一步防御此类恶意服务器的方法，并展示了防御针对已知攻击的有效性。