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Aggregation in the Mirror Space (AIMS): Fast, Accurate Distributed Machine Learning in Military Settings. (arXiv:2210.16181v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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. The existing distributed machine learning
frameworks, however, cannot realize the full benefits of DML, because they are
all based on the simple linear aggregation framework, but linear aggregation
cannot handle the $\textit{divergence challenges}$ arising in military
settings: the learning data at different devices can be heterogeneous
($\textit{i.e.}$, Non-IID data), leading to model …
aggregation arxiv distributed machine machine learning military space