Soluções para Dados Heterogêneos em Aprendizado Federado através de Similaridade de Modelos e Agrupamento de Clientes

Authors

  • Gabriel Talasso UNICAMP
  • Leandro Villas UNICAMP

DOI:

https://doi.org/10.5753/reic.2024.4649

Abstract

O aumento dos dispositivos móveis e as crescentes preocupações com a privacidade têm colocado desafios significativos na inteligência artificial distribuída. Nesse cenário, surge o Federated Learning (FL) como um método promissor em que os modelos de aprendizagem são treinados de forma colaborativa e privada. No entanto, o FL também enfrenta desafios na convergência de modelos, otimização e sobrecarga de comunicação devido a heterogeneidade nos dados e dispositivos. Nesse contexto, este trabalho relata duas soluções desenvolvidas para endereçar esse problema: 1) NeuralMatch, uma ferramenta capaz de identificar similaridades entre os clientes apenas usando os modelos e 2) FedSCCS um solução completa que utiliza dos princípios anteriores para criar múltiplos modelos por agrupamento de clientes. Ambas soluções se mostram eficientes e eficazes conforme os amplos experimentos realizados.

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Published

2024-06-28

Como Citar

Talasso, G., & Villas, L. (2024). Soluções para Dados Heterogêneos em Aprendizado Federado através de Similaridade de Modelos e Agrupamento de Clientes. Revista Eletrônica De Iniciação Científica Em Computação, 22(1), 61–70. https://doi.org/10.5753/reic.2024.4649

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Artigos