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| Main Authors: | , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.17552 |
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| _version_ | 1866916021015674880 |
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| author | Waykole, Vedant Lone, Haroon R. |
| author_facet | Waykole, Vedant Lone, Haroon R. |
| contents | Federated learning on edge devices must cope with non-IID client data and tight memory budgets. Adaptive optimizers like Adam stabilize training under data heterogeneity but require storing full-precision momentum and variance states, often tripling client memory overhead. This limits deployable model sizes and concurrent federated jobs on resource-constrained devices.
We empirically observe that momentum and variance in federated Adam exhibit fundamentally different statistical properties: momentum values are symmetric and bounded, while variance spans eight orders of magnitude with log-normal structure. Motivated by this asymmetry, we propose \textbf{Q-LocalAdam}, which applies distribution-aware 8-bit quantization block-wise linear encoding for momentum and log-space encoding for variance while keeping model parameters in full precision.
Across CIFAR-10 and CIFAR-100 under varying data heterogeneity ($α\in \{0.1, 0.5, 1.0, \text{IID}\}$), Q-LocalAdam achieves $3.37\times$ optimizer memory reduction with no accuracy loss under moderate heterogeneity and significant improvements under extreme heterogeneity (e.g., +5.74pp on CIFAR-100, $α=0.1$). Multi-seed validation confirms statistical significance ($p<0.01$). In contrast, naive uniform quantization degrades to random performance, demonstrating that distribution-aware design is essential. Q-LocalAdam enables larger models and more concurrent workloads on memory-constrained edge devices without modifying the federated protocol. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17552 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning Waykole, Vedant Lone, Haroon R. Machine Learning Federated learning on edge devices must cope with non-IID client data and tight memory budgets. Adaptive optimizers like Adam stabilize training under data heterogeneity but require storing full-precision momentum and variance states, often tripling client memory overhead. This limits deployable model sizes and concurrent federated jobs on resource-constrained devices. We empirically observe that momentum and variance in federated Adam exhibit fundamentally different statistical properties: momentum values are symmetric and bounded, while variance spans eight orders of magnitude with log-normal structure. Motivated by this asymmetry, we propose \textbf{Q-LocalAdam}, which applies distribution-aware 8-bit quantization block-wise linear encoding for momentum and log-space encoding for variance while keeping model parameters in full precision. Across CIFAR-10 and CIFAR-100 under varying data heterogeneity ($α\in \{0.1, 0.5, 1.0, \text{IID}\}$), Q-LocalAdam achieves $3.37\times$ optimizer memory reduction with no accuracy loss under moderate heterogeneity and significant improvements under extreme heterogeneity (e.g., +5.74pp on CIFAR-100, $α=0.1$). Multi-seed validation confirms statistical significance ($p<0.01$). In contrast, naive uniform quantization degrades to random performance, demonstrating that distribution-aware design is essential. Q-LocalAdam enables larger models and more concurrent workloads on memory-constrained edge devices without modifying the federated protocol. |
| title | Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.17552 |