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Abstract
We present a model-agnostic federated learning method for decentralized data with an intrinsic network structure. The network structure reflects similarities between the (statistics of) local datasets and, in turn, their associated local (“personal”) models. Our method is an instance of empirical risk minimization, with the regularization term derived from the network structure of data. In particular, we require well-connected local models, forming clusters, to yield similar predictions on a common test set. The proposed method allows for a wide range of local models. The only restriction put on these local models is that they allow for efficient implementation of regularized empirical risk minimization (training). Such implementations might be available in the form of high-level programming frameworks such as scikit-learn, Keras or PyTorch.
Original language | English |
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Title of host publication | 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings |
Publisher | IEEE |
Pages | 1614-1618 |
Number of pages | 5 |
ISBN (Electronic) | 978-9-4645-9360-0 |
DOIs | |
Publication status | Published - 2023 |
MoE publication type | A4 Conference publication |
Event | European Signal Processing Conference - Helsinki, Finland Duration: 4 Sept 2023 → 8 Sept 2023 Conference number: 31 https://eusipco2023.org/ |
Publication series
Name | European Signal Processing Conference |
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ISSN (Print) | 2219-5491 |
Conference
Conference | European Signal Processing Conference |
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Abbreviated title | EUSIPCO |
Country/Territory | Finland |
City | Helsinki |
Period | 04/09/2023 → 08/09/2023 |
Internet address |
Keywords
- complex networks
- federated learning
- heterogeneous
- non-parametric
- personalization
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Dive into the research topics of 'Towards Model-Agnostic Federated Learning over Networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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Intelligent Techniques in Condition Monitoring of Electromechanical Energy Conversion Systems
Jung, A. (Principal investigator), Tian, Y. (Project Member), Karimi, N. (Project Member) & Pfau, D. (Project Member)
01/09/2020 → 31/08/2024
Project: Academy of Finland: Other research funding