# FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

arXiv: https://arxiv.org/abs/2508.12021 · HTML: https://mlsanity.com/paper/2508.12021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss](https://mlsanity.com/paper/2106.04156/) (2021)
- [Collaborative Unsupervised Visual Representation Learning from Decentralized Data](https://mlsanity.com/paper/2108.06492/) (2021)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [A Theoretical Perspective on Hyperdimensional Computing](https://mlsanity.com/paper/2010.07426/) (2020)
- [FedX: Unsupervised Federated Learning with Cross Knowledge Distillation](https://mlsanity.com/paper/2207.09158/) (2022)
- [Distributed k-Means and k-Median Clustering on General Topologies](https://mlsanity.com/paper/1306.0604/) (2013)
- [Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients](https://mlsanity.com/paper/2204.03304/) (2022)
- [Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing](https://mlsanity.com/paper/2403.04759/) (2024)
- [Neural Network Training on In-memory-computing Hardware with Radix-4 Gradients](https://mlsanity.com/paper/2203.04821/) (2022)
- [Resource-Efficient Federated Hyperdimensional Computing](https://mlsanity.com/paper/2306.01339/) (2023)
- [Efficient Open Modification Spectral Library Searching in High-Dimensional Space with Multi-Level-Cell Memory](https://mlsanity.com/paper/2405.02756/) (2024)
- [SpecPCM: A Low-power PCM-based In-Memory Computing Accelerator for Full-stack Mass Spectrometry Analysis](https://mlsanity.com/paper/2411.09760/) (2024)
- [SparsyFed: Sparse Adaptive Federated Training](https://mlsanity.com/paper/2504.05153/) (2025)

## Abstract (arXiv)

Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challenging due to non-IID data, high computational and communication costs at edge devices, and sensitivity to communication noise. We propose FedUHD, the first UFL framework based on Hyperdimensional Computing (HDC). On the client side, FedUHD employs kNN-based cluster hypervector removal to mitigate non-IID effects by filtering detrimental local outliers. On the server side, cluster-aware HDC aggregation leverages cluster-level statistics to stabilize learning across heterogeneous clients. To further improve efficiency, we design a compute-in-memory (CIM) accelerator based on a novel phase-change memory (PCM) device, integrated with lightweight ASIC digital modules to execute the client-side HDC pipeline within the accelerator. The intrinsic robustness of HDC to low precision and device variations enables efficient mapping onto analog PCM crossbars, exploiting massive parallelism while minimizing data movement. Experimental results show that FedUHD achieves comparable accuracy to state-of-the-art neural network-based UFL methods across all datasets. On HAR and CIFAR10/100, FedUHD delivers an average 2,239x speedup and 1,542x higher energy efficiency on GPU. In addition, FedUHD reduces communication cost by up to 176x on HAR and CIFAR10/100 and demonstrates greater robustness than Orchestra under communication noise. Compared to GPU implementation of FedUHD, the proposed PCM-based accelerator provides an additional 4.07x speedup and three orders of magnitude higher energy efficiency on average. Furthermore, the results demonstrate the benefit of PCM over RRAM as a CIM substrate.