Asynchronous federated learning for tactical autonomy

Earl Tankard; Desta Haileselassie Hagos; Danda B. Rawat

doi:10.1117/12.2665404

12 June 2023 Asynchronous federated learning for tactical autonomy

Earl Tankard, Desta Haileselassie Hagos, Danda B. Rawat

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 1253811 (2023) https://doi.org/10.1117/12.2665404
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Federated learning has been an active area of research for secure and privacy-aware learning where parameters and models are shared instead of actual data. However, there are some challenges to be addressed since federated learning relies on the aggregation process for the global model. If the learning server waits to receive all updates from all clients too long for the optimal global model, there might be a high delay. In this paper, we will design, develop and evaluate asynchronous federated learning by considering asynchronous client training and asynchronous aggregation that is applicable to tactical scenarios.

Conference Presentation

Citation Download Citation

Earl Tankard, Desta Haileselassie Hagos, and Danda B. Rawat "Asynchronous federated learning for tactical autonomy", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 1253811 (12 June 2023); https://doi.org/10.1117/12.2665404

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