论文标题
免费的隐私：通过自适应功率控制的未经编码的传输进行无线联合学习
Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control
论文作者
论文摘要
联合学习（FL）是指分布式协议，这些协议在培训共同的学习任务时避免了参与设备之间的直接原始数据交换。这样，FL可以潜在地减少有关通过通信泄漏的本地数据集的信息。但是，为了提供正式的隐私保证，通常有必要建立其他掩盖机制。当FL通过未编码的传输在无线系统中实现时，通道噪声可以直接充当隐私诱导机制。本文表明，只要通过差异隐私（DP）测量的隐私约束水平低于阈值，而随着信噪比（SNR）（SNR）的降低，未编码的传输将“免费”获得隐私，即，即，而不会影响学习绩效。更一般而言，这项工作研究了无线FL中分散梯度下降的自适应功率分配（PA），目的是最大程度地减少隐私和功率约束下的学习最佳差距。研究了带有“空中计算”的正交多访问（OMA）和非正交多访问（NOMA）传输，并以封闭形式获得解决方案以进行离线优化设置。此外，提议提出启发式在线方法，以利用迭代的一步优化。通过广泛的模拟证明了动态PA的重要性以及NOMA与OMA的潜在优势。
Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL can potentially reduce the information on the local data sets that is leaked via communications. In order to provide formal privacy guarantees, however, it is generally necessary to put in place additional masking mechanisms. When FL is implemented in wireless systems via uncoded transmission, the channel noise can directly act as a privacy-inducing mechanism. This paper demonstrates that, as long as the privacy constraint level, measured via differential privacy (DP), is below a threshold that decreases with the signal-to-noise ratio (SNR), uncoded transmission achieves privacy "for free", i.e., without affecting the learning performance. More generally, this work studies adaptive power allocation (PA) for decentralized gradient descent in wireless FL with the aim of minimizing the learning optimality gap under privacy and power constraints. Both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission with "over-the-air-computing" are studied, and solutions are obtained in closed form for an offline optimization setting. Furthermore, heuristic online methods are proposed that leverage iterative one-step-ahead optimization. The importance of dynamic PA and the potential benefits of NOMA versus OMA are demonstrated through extensive simulations.
