Multi–Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197196" target="_blank" >RIV/00216305:26220/26:0197196 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10906476" target="_blank" >https://ieeexplore.ieee.org/document/10906476</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TMC.2025.3546550" target="_blank" >10.1109/TMC.2025.3546550</a>
Alternative languages
Result language
angličtina
Original language name
Multi–Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
Original language description
Federated systems enable collaborative training on highly heterogeneous, non-i.i.d. data through model personalization, which can be facilitated by employing multi-task learning. However, multi-task learning algorithms are often implemented using methods like stochastic gradient descent, which may suffer from slow convergence in a multi-task federated setting. To accelerate the training procedure, we design an efficient iterative distributed method based on the alternating direction method of multipliers (ADMM) for support vector machines (SVMs), which tackles federated classification and regression. The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i.i.d. data. To ensure data privacy, we introduce a randomization algorithm that helps avoid data inversion. Finally, we analyze the impact of the proposed privacy mechanisms and participant hardware and data heterogeneity on the system performance. Our experiments confirm the advantages of the proposed ADMM-based personalized federated multi-task learning.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Mobile Computing
ISSN
1536-1233
e-ISSN
1558-0660
Volume of the periodical
2025
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
7012-7027
UT code for WoS article
001522932600014
EID of the result in the Scopus database
—