Machine Learning in coalition settings requires combining insights available
from data assets and knowledge repositories distributed across multiple
coalition partners. In tactical environments, this requires sharing the assets,
knowledge and models in a bandwidth-constrained environment, while staying in
conformance with the privacy, security and other applicable policies for each
coalition member. Federated Machine Learning provides an approach for such
sharing. In its simplest version, federated machine learning could exchange
training data available among the different coalition members, with each
partner deciding which part of the training data from other partners to accept
based on the quality and value of the offered data. In a more sophisticated
version, coalition partners may exchange models learnt locally, which need to
be transformed, accepted in entirety or in part based on the quality and value
offered by each model, and fused together into an integrated model. In this
paper, we examine the challenges present in creating federated learning
solutions in coalition settings, and present the different flavors of federated
learning that we have created as part of our research in the DAIS ITA. The
challenges addressed include dealing with varying quality of data and models,
determining the value offered by the data/model of each coalition partner,
addressing the heterogeneity in data representation, labeling and AI model
architecture selected by different coalition members, and handling the varying
levels of trust present among members of the coalition. We also identify some
open problems that remain to be addressed to create a viable solution for
federated learning in coalition environments.

本文探讨了联邦学习在联盟环境中的挑战，提出了针对数据和模型质量变化、异构性、标签以及不同联盟成员对 AI 模型架构的选择等问题的解决方案。同时，文章也强调了联邦学习在实现联盟环境中可行的解决方案方面仍存在的一些开放性问题。