# Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

arXiv: https://arxiv.org/abs/2403.16398 · HTML: https://mlsanity.com/paper/2403.16398/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Understanding Dimensional Collapse in Contrastive Self-supervised Learning](https://mlsanity.com/paper/2110.09348/) (2021)
- [On Feature Decorrelation in Self-Supervised Learning](https://mlsanity.com/paper/2105.00470/) (2021)
- [Revisiting Training Strategies and Generalization Performance in Deep Metric Learning](https://mlsanity.com/paper/2002.08473/) (2020)
- [Federated Unsupervised Representation Learning](https://mlsanity.com/paper/2010.08982/) (2020)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Divergence-aware Federated Self-Supervised Learning](https://mlsanity.com/paper/2204.04385/) (2022)
- [Collaborative Unsupervised Visual Representation Learning from Decentralized Data](https://mlsanity.com/paper/2108.06492/) (2021)
- [Sinkhorn Divergences for Unbalanced Optimal Transport](https://mlsanity.com/paper/1910.12958/) (2019)
- [Federated Learning Meets Multi-objective Optimization](https://mlsanity.com/paper/2006.11489/) (2020)
- [Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.00226/) (2022)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [Understanding Collapse in Non-Contrastive Siamese Representation Learning](https://mlsanity.com/paper/2209.15007/) (2022)
- [FedX: Unsupervised Federated Learning with Cross Knowledge Distillation](https://mlsanity.com/paper/2207.09158/) (2022)
- [L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2307.07393/) (2023)
- [VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution](https://mlsanity.com/paper/2304.01434/) (2023)
- [Federated Learning without Full Labels: A Survey](https://mlsanity.com/paper/2303.14453/) (2023)
- [ProtoFL: Unsupervised Federated Learning via Prototypical Distillation](https://mlsanity.com/paper/2307.12450/) (2023)
- [HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning](https://mlsanity.com/paper/2307.14384/) (2023)

## Built on by (most cited first, 13 of 13)

- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data](https://mlsanity.com/paper/2503.06916/) (2025)
- [FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2506.16218/) (2025)
- [FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2502.21012/) (2025)
- [Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation](https://mlsanity.com/paper/2503.23507/) (2025)
- [Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data](https://mlsanity.com/paper/2607.02447/) (2026)
- [Federated Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2505.23292/) (2025)
- [Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning](https://mlsanity.com/paper/2508.01251/) (2025)
- [Cooperative Pseudo Labeling for Unsupervised Federated Classification](https://mlsanity.com/paper/2510.10100/) (2025)
- [FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements](https://mlsanity.com/paper/2510.12132/) (2025)
- [Semantic-based Distributed Learning for Diverse and Discriminative Representations](https://mlsanity.com/paper/2604.18237/) (2026)
- [Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis](https://mlsanity.com/paper/2605.07888/) (2026)
- [Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation](https://mlsanity.com/paper/2610.04700/) (2026)

## Abstract (arXiv)

Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existing FUSL methods suffers from insufficient representations, i.e., (1) representation collapse entanglement among local and global models, and (2) inconsistent representation spaces among local models. The former indicates that representation collapse in local model will subsequently impact the global model and other local models. The latter means that clients model data representation with inconsistent parameters due to the deficiency of supervision signals. In this work, we propose FedU2 which enhances generating uniform and unified representation in FUSL with non-IID data. Specifically, FedU2 consists of flexible uniform regularizer (FUR) and efficient unified aggregator (EUA). FUR in each client avoids representation collapse via dispersing samples uniformly, and EUA in server promotes unified representation by constraining consistent client model updating. To extensively validate the performance of FedU2, we conduct both cross-device and cross-silo evaluation experiments on two benchmark datasets, i.e., CIFAR10 and CIFAR100.