Использование похожести данных для улучшения качества персонализации с помощью внедрения информации с других устройств

Современные приложения федеративного обучения часто требуют персонализированного результата для каждого пользователя. Обучение на локальных данных нецелесообразно из-за малых выборок и ограниченной вычислительной мощности устройств, тогда как глобальное обучение неэффективно из-за неоднородности данных у клиентов. Озвученные проблемы формируют значимую исследовательскую задачу. В работе предлагается новая стратегия, которая задействует мощный сервер в качестве помощника. Сервер использует свой большой набор данных для отбора объектов для каждого устройства на основе косинусной похожести, что обеспечивает более эффективную персонализацию без потери качества обучения. Выводы работы обоснованы теоретически и подтверждены серией экспериментов, включая предсказание следующего слова и классификацию изображений. Представленный подход превосходит большинство передовых методов как теоретически, так и эмпирически.

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