aiqbench analysis
FedUHD introduces a novel framework combining unsupervised federated learning with hyperdimensional computing to address privacy and efficiency challenges in distributed training. By leveraging phase-change memory accelerators, the system achieves massive speedups and energy savings while maintaining accuracy comparable to traditional neural networks on edge devices. This approach significantly reduces communication costs and demonstrates robustness against non-IID data and noise, marking a practical step toward scalable, low-power AI deployment.
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
arXiv:2508.12021v2 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
arXiv:2508.12021 [cs.LG]
(or arXiv:2508.12021v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2508.12021 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/TCASAI.2026.3727571 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 345 words
