Federated learning (FL) is a type of distributed machine learning at the wireless edge that preserves the privacy of clients' data from adversaries and even the central server. Existing federated learning approaches either use (i) secure multiparty computation (SMC) which is vulnerable to inference or (ii) differential privacy which may decrease the test accuracy given a large number of parties with relatively small amounts of data each. To tackle the problem with the existing methods in the literature, In this paper, we introduce incorporate federated learning in the inner-working of MIMO systems.

翻译：联邦学习（FL）是一种在无线边缘执行的分布式机器学习方法，能够保护客户端数据免受对手甚至中央服务器的隐私泄露。现有联邦学习方法要么采用（i）易受推理攻击的安全多方计算（SMC），要么采用（ii）差分隐私——当参与方数量庞大且各方案数据量较小时，该方法可能降低测试准确率。为解决现有文献方法的上述问题，本文提出将联邦学习集成到MIMO系统的内部工作机制中。