The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative
architecture has been proposed to compensate for the lack of data processing power of …

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users'
local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless …

Federated learning data is drawn from a distribution of distributions: clients are drawn from a
meta-distribution, and their data are drawn from local data distributions. Thus generalization …

Gradient compression is a widely-established remedy to tackle the communication
bottleneck in distributed training of large deep neural networks (DNNs). Under the error …

As neural networks get widespread adoption in resource-constrained embedded devices,
there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs) are …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Abstract In Federated Learning (FL), a common approach for aggregating local solutions
across clients is periodic full model averaging. It is, however, known that different layers of …

Secure aggregation is a popular protocol in privacy-preserving federated learning, which
allows model aggregation without revealing the individual models in the clear. On the other …