Emerging distributed applications recently boosted the development of decentralized machine learning, especially in IoT and edge computing fields.
Distributed machine learning (DML) can be an important capability for modern military to take advantage of data and devices distributed at multiple vantage points to adapt and learn.
In this paper, we analyze properties of the WPM and rigorously prove convergence properties of our aggregation mechanism.
The core of Vulcan is a novel, compact graph embedding that transforms highdimensional graph structure data (i. e., path-changed information) into a low-dimensional vector representation.
To solve those issues, we present a sentiment analysis model named Isomer, which performs a dual-channel attention on heterogeneous dependency graphs incorporating external knowledge, to effectively integrate other additional information.
Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel.
In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters.