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Федеративное обучение диффузионных моделей с обрезкой и иерархией — экономим ресурсы, побеждаем гетерогенность данных

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В мире ИИ диффузионные модели (DM) - это как супергерои генерации изображений. Они создают картинки лучше, чем GAN или VAE, и используются везде: от компьютерного зрения до генерации текста. Но представьте: вы хотите обучить такую модель на данных с кучи устройств (смартфоны, edge‑устройства), не сливая личные данные в один котел. Вот тут вступает федеративное обучение (FL) — парадигма, где модели учатся распределенно, сохраняя приватность.

Проблема? В FL данные часто неоднородны (non‑IID), коммуникация жрет трафик, а модели огромные. Исследование из arXiv предлагает крутое решение: FedPhD. Это не просто метод — это комбо из иерархического FL, умной агрегации и структурированной обрезки моделей. Результат? Лучшее качество изображений (FID улучшен на 34%), минус 88% коммуникаций и 44% вычислений. Звучит вирусно? Давайте разберем по полочкам, чтобы было понятно даже новичку в ML.

Почему диффузионные модели — это круто, но проблемно в FL?

Диффузионные модели (как DDPM или DDIM) работают по принципу «шум туда‑сюда». Они добавляют шум к изображению (forward process), а потом его убирают (reverse process), генерируя новые картинки из чистого шума. Формулы выглядят страшно:

pθ(x)=∫pθ(x0:T) dx1:Tpθ​(x)=∫pθ​(x0:T​)dx1:T​

Но на деле это как варка супа: постепенно «варим» шум в изображение. DM круты в генерации высококачественных изображений, но требуют тонны данных и вычислений.

В федеративном обучении (FL, спасибо Google за Gboard) модели учатся на клиентах (устройствах), а сервер агрегирует обновления без сырых данных. Плюсы: приватность, доступ к «островам данных». Минусы для DM:

Гетерогенность данных (non‑IID): У одного клиента — котики, у другого — машины. Градиенты расходятся, качество падает. В экспериментах на CIFAR-10 FID (метрика качества, чем ниже — тем лучше) с IID‑данными 11.98, с non‑IID — 21.63. Centralized обучение выигрывает.

Коммуникация и вычисления: Модели большие (U‑Net с 35.7M параметров), трафик огромный.

Раньше пытались: Phoenix делится данными (но приватность?), FedDiffuse «режет» обновления. Но никто не решил все разом.

FedPhD: Иерархия, обрезка и умная агрегация — рецепт успеха

Авторы (из Университета Глазго и Hunan University) вводят FedPhD. Ключевые фичи:

  1. Иерархическое федеративное обучение (HFL): Добавляем слой edge‑серверов между клиентами и центральным сервером. Почему? Частая агрегация на edge минимизирует расхождение от non‑IID (как в ShapeFL). Клиенты шлют обновления на edge чаще (каждый re раунд), edge — на центр реже (каждый rg=5 раунд). Результат: меньше трафика на дальние расстояния.

  2. Структурированная обрезка (pruning): Не просто «выкинуть веса» (unstructured), а удалять целые группы (каналы, слои) для совместимости с CPU/GPU. Используют DepGraph для зависимостей и group‑norm для критериев. Два варианта:

    From scratch (OS): Обрезаем сразу, случайно по L2-норме.

    After sparse training: После Rs раундов, по group‑norm.

    При обрезке 44% — параметры падают с 35.7M до 20.3M, MACs (вычисления) с 6.1G до 3.4G. Коммуникация — минус 44%.

  3. Homogeneity‑aware агрегация и выбор сервера: Чтобы бороться с non‑IID, вводят Statistical Homogeneity (SH) score:

    μn=2−∑y∈Y∣qn(y)−qu(y)∣2μn​=2−∑y∈Y​∣qn​(y)−qu​(y)∣2​

    Где qn(y)qn​(y) — распределение меток на клиенте, ququ​ — uniform. Edge‑сервера накапливают распределения клиентов, клиенты выбирают edge по вероятности (баланс нагрузки + SH). Агрегация весит по SH + размеру данных:

    ρe=ReLU(ne+a⋅μe+b)∑ReLU(ne+a⋅μe+b)ρe​=∑ReLU(ne​+a⋅μe​+b)ReLU(ne​+a⋅μe​+b)​

    Это обеспечивает «гомогенность» — модель ближе к IID.

Алгоритм в псевдокоде — в статье, но суть: клиенты тренируют локально, шлют на edge, edge агрегирует, центр — глобально. Приватность: только распределения меток, без сырых данных (и планы на DP‑шум).

Эксперименты: FedPhD бьет базлайны по всем фронтам

Тестировали на CIFAR-10 (50K изображений 32×32) и CelebA (163K, 64×64) с DDIM (100 шагов). Настройки: 20 клиентов, 2 edge, non‑IID (каждый клиент — 2 класса в CIFAR, 1 в CelebA).

Качество: FID/IS лучше на 34%+ vs FedAvg, FedProx, MOON, SCAFFOLD, FedDiffuse. На CIFAR-10 FID 16.74 (vs 21.63 у FedAvg), IS 4.24. На CelebA FID 7.48, IS 2.88.

Метод

FID (CIFAR-10) ↓

IS (CIFAR-10) ↑

FID (CelebA) ↓

IS (CelebA) ↑

FedAvg

21.63

4.16

11.29

2.81

FedProx

22.42

4.01

13.39

2.55

FedDiffuse

25.38

3.66

15.42

2.34

MOON

20.43

4.17

10.88

2.78

SCAFFOLD

29.54

3.54

21.44

2.38

FedPhD

16.74

4.24

7.48

2.88

FedPhD (OS)

17.03

4.14

7.48

2.88

Эффективность: Минус 44% параметров, 40% вычислений, 88% коммуникаций

При разных pruning‑ratio: До 44% — качество почти не падает, дальше — trade‑off.

Графики (FID vs epochs) показывают: FedPhD сходится быстрее, особенно на non‑IID.

Почему это важно для реального мира?

Применения: Edge‑AI (IoT, автономные авто), приватные данные (медицина, финансы). DM как аугментация для FL‑задач.

Будущее: Авторы обещают EMA в HFL, больше приватности (DP, MPC).

Для разработчиков: Код, наверное, скоро на GitHub (пока под ревью в IEEE). Пробуйте на PyTorch с RTX.

FedPhD — шаг к «демократизации» ИИ: учим мощные модели на слабых устройствах, без облачных монстров. Если вы в ML/FL — must‑read. Делитесь мыслями: пробовали DM в FL? Что думаете о pruning?

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  • 01:12 harristhomas7376

    "In the crypto world, this is great news I want to share. Last year, I fell victim to a scam disguised as a safe investment option. I have invested in crypto trading platforms for about 10yrs thinking I was ensuring myself a retirement income, only to find that all my assets were either frozen, I believed my assets were secure — until I discovered that my BTC funds had been frozen and withdrawals were impossible. It was a devastating moment when I realized I had been scammed, and I thought my Bitcoin was gone forever, Everything changed when a close friend recommended the Capital Crypto Recover Service. Their professionalism, expertise, and dedication enabled me to recover my lost Bitcoin funds back — more than €560.000 DEM to my BTC wallet. What once felt impossible became a reality thanks to their support. If you have lost Bitcoin through scams, hacking, failed withdrawals, or similar challenges, don’t lose hope. I strongly recommend Capital Crypto Recover Service to anyone seeking a reliable and effective solution for recovering any wallet assets. They have a proven track record of successful reputation in recovering lost password assets for their clients and can help you navigate the process of recovering your funds. Don’t let scammers get away with your hard-earned money – contact Email: [email protected] Phone CALL/Text Number: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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