# FedEmbed: Personalized Private Federated Learning

arXiv: https://arxiv.org/abs/2202.09472 · HTML: https://mlsanity.com/paper/2202.09472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [Lower Bounds and Optimal Algorithms for Personalized Federated Learning](https://mlsanity.com/paper/2010.02372/) (2020)
- [Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications](https://mlsanity.com/paper/2102.08503/) (2021)
- [Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations](https://mlsanity.com/paper/1906.06397/) (2019)
- [Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors](https://mlsanity.com/paper/1903.10304/) (2019)
- [SapAugment: Learning A Sample Adaptive Policy for Data Augmentation](https://mlsanity.com/paper/2011.01156/) (2020)
- [Personalized Federated Deep Learning for Pain Estimation From Face Images](https://mlsanity.com/paper/2101.04800/) (2021)
- [Embedding-Based Speaker Adaptive Training of Deep Neural Networks](https://mlsanity.com/paper/1710.06937/) (2017)
- [Spatio-Temporal Split Learning](https://mlsanity.com/paper/2108.06309/) (2021)
- [Embeddings for DNN speaker adaptive training](https://mlsanity.com/paper/1909.13537/) (2019)
- [Mirroring to Build Trust in Digital Assistants](https://mlsanity.com/paper/1904.01664/) (2019)

## Built on by (most cited first, 3 of 3)

- [SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning](https://mlsanity.com/paper/2405.20127/) (2024)
- [Federated Representation Learning via Maximal Coding Rate Reduction](https://mlsanity.com/paper/2210.00299/) (2022)
- [FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings](https://mlsanity.com/paper/2210.03766/) (2022)

## 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.