Communication-efficient federated learning

Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL.

Parametric Fault Diagnosis of Very High-Frequency Circuits Containing Distributed Parameter Transmission Lines

Parametric fault diagnosis of analog very high-frequency circuits consisting of a distributed parameter transmission line (DPTL) terminated at both ends by lumped one-ports is considered in this paper. The one-ports may include linear passive and active components. The DPTL is a uniform two-conductor line immersed in a homogenous medium, specified by the per-unit-length (p-u-l) parameters. The proposed method encompasses all aspects of parametric fault diagnosis: detection of the faulty area, location of the fault inside this area, and estimation of its value. It is assumed that only one fault can occur in the circuit. The diagnostic method is based on a measurement test arranged in the AC state. Different approaches are proposed depending on whether the faulty is DPTL or one of the one-ports. An iterative method is modified to solve various systems of nonlinear equations that arise in the course of the diagnostic process. The diagnostic method can be extended to a broader class of circuits containing several transmission lines. Three numerical examples reveal that the proposed diagnostic method is fast and gives quite accurate findings.