mlsanityData to 7 Oct 2026

FedEmbed: Personalized Private Federated Learning

arXiv 2202.09472 ↗First posted 18 Feb 2022cs.LGcited by 3

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Abstract (arXiv)

Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical. Adding differential privacy guarantees bounds on privacy while data are contributed to a global model. Adding personalization to federated learning introduces new challenges as we must account for preferences of individual users, where a data sample could have conflicting labels because one sub-population of users might view an input positively, but other sub-populations view the same input negatively. We present FedEmbed, a new approach to private federated learning for personalizing a global model that uses (1) sub-populations of similar users, and (2) personal embeddings. We demonstrate that current approaches to federated learning are inadequate for handling data with conflicting labels, and we show that FedEmbed achieves up to 45% improvement over baseline approaches to personalized private federated learning.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 40 references that are in the map. First posted on arXiv 18 Feb 2022.

Builds on

25 of 40 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  4. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  9. 2019Federated Learning with Personalization Layerscited by 533
  10. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  11. 2020Adaptive Personalized Federated Learningcited by 353
  12. 2018Distributed learning of deep neural network over multiple agentscited by 277
13 more
  1. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  2. 2018Characterizing and Avoiding Negative Transfercited by 226
  3. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  4. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  5. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  6. 2019Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrationscited by 14
  7. 2019Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviorscited by 9
  8. 2020SapAugment: Learning A Sample Adaptive Policy for Data Augmentationcited by 9
  9. 2021Personalized Federated Deep Learning for Pain Estimation From Face Imagescited by 8
  10. 2017Embedding-Based Speaker Adaptive Training of Deep Neural Networkscited by 7
  11. 2021Spatio-Temporal Split Learningcited by 7
  12. 2019Embeddings for DNN speaker adaptive trainingcited by 4
  13. 2019Mirroring to Build Trust in Digital Assistantscited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2024SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learningcited by 3
  2. 2022Federated Representation Learning via Maximal Coding Rate Reductioncited by 1
  3. 2022FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddingscited by 0