6 accepted papers
Federated graph learning (FGL) empowers distributed training of subgraphs across multiple institutions, overcoming the challenges of data silos and inter-institutional data sharing. Existing federated subgraph methods achieve collaborative training by training local models on clients and uploading t…
Hierarchical federated learning (HFL) has emerged as the architecture of choice for multi-level communication networks, mainly because of its data privacy protection and low communication cost. However, existing studies on the convergence analysis for HFL are limited to the assumptions of full worke…
We investigate fast data aggregation via over-the-air computation (AirComp) over wireless networks. In this scenario, an access point (AP) with multiple antennas aims to recover the arithmetic mean of sensory data from multiple wireless devices. To minimize estimation distortion, we formulate a mean…
Fog computing brings computation and services to the edge of networks enabling real time applications. In order to provide satisfactory quality of experience, the latency of fog networks needs to be minimized. In this paper, we consider a peer computation offloading problem for a fog network with un…
Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation. Efficient computation peer offloading is critical to fully utilize the computational resources in fog networks. In this paper, we consider computation peer offloading problem in a fog network with time…
In wireless sensor networks (WSN s), energy is always precious for sensor nodes. To save energy, censoring is introduced to cut the total number of transmission by only transmitting informative data. This algorithm, however, ignores the energy consumption during the delivery of parameters, which can…