Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning

• URL: http://arxiv.org/abs/2605.17552v1
• Date: Sun, 17 May 2026 17:23:23 GMT
• Title: Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
• Abstract summary: Federated learning on edge devices must cope with non-IID client data and tight memory budgets.<n>We propose textbfQ-LocalAdam, which applies distribution-aware 8-bit quantization block-wise linear encoding for momentum and log-space encoding for variance.
• Score: 1.4610038284393168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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