论文标题
PHY喂养：无线通信中联邦学习中的信息理论的安全聚合
PHY-Fed: An Information-Theoretic Secure Aggregation in Federated Learning in Wireless Communications
论文作者
论文摘要
联合学习（FL）是一种在无线边缘的分布式机器学习，可保留客户从对手甚至中央服务器中的数据的隐私。现有的联合学习方法要么使用（i）安全的多方计算（SMC），该计算容易受到推理或（ii）差异隐私的攻击，因此，鉴于大量的各方，每个方案都可能会降低测试准确性，每个缔约方都有相对较少的数据。为了通过文献中现有的方法解决问题，在本文中，我们介绍了Phy-fed，这是一个新的框架，从信息理论的角度来确保联合算法。
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 PHY-Fed, a new framework that secures federated algorithms from an information-theoretic point of view.
