2022 18th International Conference on Mobility, Sensing and Networking (MSN) / IEEE Computer Society.

Format:

Book

Author/Creator:

IEEE Computer Society, author, issuing body.

Language:

English

Subjects (All):

Sensor networks.

Sensor networks--Congresses.

Physical Description:

1 online resource

Other Title:

2022 18th International Conference on Mobility, Sensing and Networking

Place of Publication:

Piscataway, NJ : IEEE Computer Society, 2023.

Summary:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781665464574

1665464577