mlsanityData to 7 Oct 2026

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning

arXiv 2402.12537 ↗First posted 19 Feb 2024cs.LGcited by 1

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored. We initiate a systematic study of such personalized unsupervised learning by developing algorithms based on optimization criteria inspired by a hierarchical Bayesian statistical framework. We develop adaptive algorithms that discover the balance between using limited local data and collaborative information. We do this in the context of two unsupervised learning tasks: personalized dimensionality reduction and personalized diffusion models. We develop convergence analyses for our adaptive algorithms which illustrate the dependence on problem parameters (e.g., heterogeneity, local sample size). We also develop a theoretical framework for personalized diffusion models, which shows the benefits of collaboration even under heterogeneity. We finally evaluate our proposed algorithms using synthetic and real data, demonstrating the effective sample amplification for personalized tasks, induced through collaboration, despite data heterogeneity.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 34 of its 46 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

34 of 46 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks28 Nov

Other references · 33

  1. 2020Denoising Diffusion Implicit Modelscited by 9,655
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  6. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  7. 2019Advances and Open Problems in Federated Learningcited by 3,021
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  9. 2015Variational Inference with Normalizing Flowscited by 2,161
  10. 2018Demystifying MMD GANscited by 1,268
  11. 2017Federated Multi-Task Learningcited by 669
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 612
21 more
  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  4. 2020Adaptive Personalized Federated Learningcited by 353
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  7. 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentationcited by 293
  8. 2020Few-Shot Learning via Learning the Representation, Provablycited by 213
  9. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  10. 2023Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningcited by 191
  11. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  12. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  13. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
  14. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  15. 2019Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphscited by 34
  16. 2021Decentralized Riemannian Gradient Descent on the Stiefel Manifoldcited by 33
  17. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  18. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  19. 2022Self-Aware Personalized Federated Learningcited by 16
  20. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  21. 2022Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoderscited by 4

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025SPIRE: Conditional Personalization for Federated Diffusion Generative Modelscited by 0

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  1. 2022Adaptive Expert Models for Personalization in Federated Learning
  2. 2024pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
  3. 2026Global Federated Learning Strategies for Building Efficient Personalized Models
  4. 2025WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models
  5. 2025Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
  6. 2020Specialized federated learning using a mixture of experts
  7. 2025FedMerge: Federated Personalization via Model Merging
  8. 2024pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning