DAQSyn: A decentralized adaptive quantization-aware synchronous framework in heterogeneous on-device AI networks

Decentralized Federated Learning (DFL) has emerged as a key paradigm for collaborative model training across distributed edge devices while preserving data privacy and system autonomy. However, the performance of existing DFL frameworks is constrained by synchronization delays, high communication overhead, and poor scalability in heterogeneous environments. Conventional methods often rely on fixed-precision quantization and uniform synchronization, which overlook device diversity and data heterogeneity, leading to slower convergence and inefficient resource utilization. To address these limitations, this paper presents the Decentralized Adaptive Quantization-Aware Synchronous (DAQSyn) framework, a unified resource-aware learning strategy that integrates adaptive quantization, synchronization, and performance-weighted aggregation. In DAQSyn, each device autonomously adjusts its model precision according to its computational and communication capabilities, generating lightweight models that enhance convergence speed while preserving performance. The synchronization ensures efficient coordination among devices through a BSP-based barrier that harmonizes update timing, reducing idle waiting time by approximately 19% compared to FP16 and over 25% compared to low-bit quantization. The performance-weighted aggregation mechanism further enhances model stability by prioritizing high-quality local updates, leading to an accuracy improvement of 1% and a 2.5 ×  faster convergence relative to fixed-precision approaches. Combined, these mechanisms enable lightweight models with reduced communication cost and robust learning performance across heterogeneous devices and non-IID data settings.