Collaboration without Data Sharing Made Possible

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A US Army researcher and collaborators have developed artificially intelligent techniques that will enhance Soldiers’ situational awareness in the multi-domain operating environment.

The research will enable secure, dynamic and semantically-aware distributed analytics for deriving situational understanding in coalition operations, where data sharing may be prescribed by policy constraints. This research further extends the capability and applicability of federated learning, a term initially coined by Google.

Federated learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. Federated learning enables multiple actors to build a common, robust machine learning model without sharing data.

The paper and research address several important problems in federated learning, or FL, for the first time, including training optimization under resource constraints, and technique validation by implementation using real-world edge devices. 

“In terms of implications for defense applications, this new technology enables distributed training or adaptation of analytics models in resource-constrained environments, to allow coalition partners (or military units) to help each other learn similar tasks without the need of sharing their sensitive data due to privacy considerations or lack of communication resources,” said Professor Kin Leung, Electrical and Electronic Engineering, and Computing Departments at Imperial College London. “The new approach provides the cutting-edge capability over our adversaries.”

The Institute of Electrical and Electronics Engineers Communications Society recognized the research work.