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：Federated learning（FL）是一种分布式机器学习方法，能够保护客户端数据的隐私性免受攻击者和中心服务器的侵犯。现有的联邦学习方法要么使用易受推理攻击的安全多方计算（SMC），要么使用差分隐私，但在相对较小的数据集上使用大量的组参与会导致测试准确性的下降。为了解决现有方法在文献中存在的问题，本文将联邦学习方法引入到MIMO系统的内部运作中。