论文标题
私人无线联合学习，并使用匿名的无线计算
Private Wireless Federated Learning with Anonymous Over-the-Air Computation
论文作者
论文摘要
在常规联合学习（FL）中，可以通过向本地模型更新注入其他噪声来获得差异隐私（DP）保证，然后再将其传输到参数服务器（PS）。在无线FL方案中，我们表明，可以通过利用无线计算（OAC）并匿名将传输设备匿名来提高系统的隐私。在OAC中，设备以未编码的方式同时传输其模型更新，从而更有效地使用了可用频谱。我们进一步利用OAC为传输设备提供匿名性。提出的方法通过减少必须注入的噪声量来改善私人无线FL的性能。
In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained by injecting additional noise to local model updates before transmitting to the parameter server (PS). In the wireless FL scenario, we show that the privacy of the system can be boosted by exploiting over-the-air computation (OAC) and anonymizing the transmitting devices. In OAC, devices transmit their model updates simultaneously and in an uncoded fashion, resulting in a much more efficient use of the available spectrum. We further exploit OAC to provide anonymity for the transmitting devices. The proposed approach improves the performance of private wireless FL by reducing the amount of noise that must be injected.
