Secure federated machine learning for distributed spectrum sensing in communication networks

Richard Searle; Prabhanjan Gururaj; Shreyas Gaikwad; Kiran Kannur

doi:10.1117/12.2663765

12 June 2023 Secure federated machine learning for distributed spectrum sensing in communication networks

Richard Searle, Prabhanjan Gururaj, Shreyas Gaikwad, Kiran Kannur

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 1253810 (2023) https://doi.org/10.1117/12.2663765
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Federated machine learning (FML) has proved a useful technique for training of artificial intelligence and machine learning (AI/ML) models, using data that is distributed among different constituents of a network which may be geographically dispersed. Typically, the data privacy of individual constituents should be preserved, and it may also be desirable to protect the integrity and secrecy of the algorithms and trained models deployed within the network. Demonstrating the privacy-enhancing technology of Confidential Computing, we present the results obtained using a novel solution for FML implementation that supports model training within a distributed network of data providers. Based upon recent research on the use of FML for distributed spectrum sensing in communication networks, we demonstrate the application of the proposed solution for distributed model training within a simulated sensor network of arbitrary topology. The presented solution provides for graph-based network configuration and model convergence within decentralized network applications. Cross-domain adaptation of the proposed solution and characteristics of confidential computing that support a zero-trust architecture are discussed, along with the integrated model integrity protection provided by attestation of trusted execution environments (TEEs). We conclude by looking ahead to the application of our solution to model training within distributed communications networks and sensor arrays, characterized by devices with limited electrical and computational power. We consider the use of physical unclonable functions (PUFs) to encrypt raw data before processing within a layered hierarchy secured with Confidential Computing technology.

Conference Presentation

Citation Download Citation

Richard Searle, Prabhanjan Gururaj, Shreyas Gaikwad, and Kiran Kannur "Secure federated machine learning for distributed spectrum sensing in communication networks", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 1253810 (12 June 2023); https://doi.org/10.1117/12.2663765

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