Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even so, FL has some privacy vulnerabilities that have been tried to be overcome by applying some techniques like Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). However, these techniques have some important drawbacks that might narrow their range of application: problems to work with non-linear functions and to operate large matrix multiplications and high communication and computational costs to manage semi-honest nodes. In this context, we propose a solution to guarantee privacy in FL schemes that simultaneously solves the previously mentioned problems. Our proposal is based on the Berrut Approximated Coded Computing, a technique from the Coded Distributed Computing paradigm, adapted to a Secret Sharing configuration, to provide input privacy to FL in a scalable way. It can be applied for computing non-linear functions and treats the special case of distributed matrix multiplication, a key primitive at the core of many automated learning tasks. Because of these characteristics, it could be applied in a wide range of FL scenarios, since it is independent of the machine learning models or aggregation algorithms used in the FL scheme. We provide analysis of the achieve privacy and complexity of our solution and, due to the extensive numerical results performed, it can be observed a good trade-off between privacy and precision.

翻译：联邦学习（FL）是一种有趣的策略，允许多个数据所有者在不泄露私有数据集的情况下协作训练AI模型。即便如此，联邦学习仍存在一些隐私漏洞，研究者尝试通过差分隐私、同态加密或安全多方计算等技术来克服。然而，这些技术存在一些重要缺陷，可能限制其应用范围：难以处理非线性函数和大规模矩阵乘法，以及在管理半诚实节点时产生高通信和计算成本。在此背景下，我们提出一种解决方案，可同时解决上述问题，保障联邦学习方案中的隐私性。我们的方案基于贝鲁特近似编码计算——一种源自编码分布式计算范式的技术，并适配秘密共享配置，以可扩展的方式为联邦学习提供输入隐私。该方法可应用于计算非线性函数，并特别处理分布式矩阵乘法这一许多自动化学习任务中的核心原语。由于这些特性，该方案可适用于广泛的联邦学习场景，因为它独立于联邦学习方案中使用的机器学习模型或聚合算法。我们对所提出方案的隐私性和复杂度进行了分析，通过大量数值结果可以观察到隐私性与精度之间的良好权衡。