2022 18th International Conference on Mobility, Sensing and Networking (MSN) /

With the advancement in Machine Learning (ML) techniques, a wide range of applications that leverage ML have emerged across research, industry, and society to improve application performance. However, existing ML schemes used within such applications struggle to attain high model accuracy due to the...

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Weitere Titel:
2022 18th International Conference on Mobility, Sensing and Networking
Körperschaft:
IEEE Computer Society
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Piscataway, NJ : IEEE Computer Society, 2023.
Zusammenfassung:
With the advancement in Machine Learning (ML) techniques, a wide range of applications that leverage ML have emerged across research, industry, and society to improve application performance. However, existing ML schemes used within such applications struggle to attain high model accuracy due to the heterogeneous and distributed nature of their generated data, resulting in reduced model performance. In this paper we address this challenge by proposing PPFM: an adaptive and hierarchical Peer-to-Peer Federated Meta-learning framework. Instead of leveraging a conventional static ML scheme, PPFM uses multiple learning loops to dynamically self-adapt its own architecture to improve its training effectiveness for different generated data characteristics. Such an approach also allows for PPFM to remove reliance on a fixed centralized server in a distributed environment by utilizing peer-to-peer Federated Learning (FL) framework. Our results demonstrate PPFM provides significant improvement to model accuracy across multiple datasets when compared to contemporary ML approaches.
Umfang:
1 online resource
ISBN:
9781665464574
1665464577
Schlagworte:
Sensor networks.
Sensor networks > Congresses.
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