Towards developing a data security aware federated training framework in multi-modal contested environments

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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12113/121130M/Towards-developing-a-data-security-aware-federated-training-framework-in/10.1117/12.2618904.short?SSO=1

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https://doi.org/10.1117/12.2618904
 http://hdl.handle.net/11603/25142

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Author/Creator ORCID

https://orcid.org/0000-0002-1290-0378

Date

2022-06-06

Type of Work

conference papers and proceedings
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Citation of Original Publication

Pretom Roy Ovi, Emon Dey, Nirmalya Roy, Aryya Gangopadhyay, and Robert F. Erbacher "Towards developing a data security aware federated training framework in multi-modal contested environments", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130M (6 June 2022); https://doi.org/10.1117/12.2618904

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

Secure data communication is crucial in contested environments such as battlefields. In such environments, there is always risk of data breach through unauthorized interceptions. This may lead to unauthorized access to tactical information and infiltration into the systems. In this work, we propose a detailed training setup in the federated learning framework for object classification where the raw data will be maintained locally at the edge devices and will not be shared with a central server or with each other. The server sends a global model to edge devices, which is then trained locally at the edge, and the updated parameters are sent back to the central server, where they are aggregated, which takes place iteratively. This setup ensures robustness against malicious cyberattacks as well as reduce communication overhead. Furthermore, to tackle the irregularity in object classification task with a single data modality in such contested environment, a deep learning model incorporating multiple modalities is used as the global model in our proposed federated learning setup. This model can serve as a possible solution in object identification with multi-modal data. We conduct a comprehensive analysis on the importance of multi-modal approach compared to individual modalities within our proposed federate learning setup. We also provide a resource profiling based on memory requirements, training time, and energy usage on two resource constrained devices to demonstrate the feasibility of the proposed approach.