Game-theoretic distributed learning of generative models for heterogeneous data collections

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00389618" target="_blank" >RIV/68407700:21230/25:00389618 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/FLLM67465.2025.11391178" target="_blank" >https://doi.org/10.1109/FLLM67465.2025.11391178</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FLLM67465.2025.11391178" target="_blank" >10.1109/FLLM67465.2025.11391178</a>

Result language

angličtina

Original language name

Game-theoretic distributed learning of generative models for heterogeneous data collections

Original language description

One of the main challenges in distributed learning arises from the difficulty of handling heterogeneous local models and data. In light of the recent success of generative models, we propose to meet this challenge by building on the idea of exchanging synthetic data instead of sharing model parameters. Local models can then be treated as "black boxes" with the ability to learn their parameters from data and to generate data according to these parameters. Moreover, if the local models admit semi-supervised learning, we can extend the approach by enabling local models on different probability spaces. This allows to handle heterogeneous data with different modalities. We formulate the learning of the local models as a cooperative game starting from the principles of game theory. We prove the existence of a unique Nash equilibrium for exponential family local models and show that the proposed learning approach converges to this equilibrium. We demonstrate the advantages of our approach on standard benchmark vision datasets for image classification and conditional generation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2025 3rd International Conference on Foundation and Large Language Models (FLLM)

ISBN

979-8-3315-9410-7

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

153-160

Publisher name

IEEE

Place of publication

Anchorage, Alaska

Event location

Vienna

Event date

Nov 25, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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