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DAQSyn: A decentralized adaptive quantization-aware synchronous framework in heterogeneous on-device AI networks
Misbah Bibi1, Qazi Waqas Khan1, Syed Ali Yazdan1
1Department of Computer Engineering, Jeju National University, Jeju, 63243, Republic of Korea.
Summary
Decentralized Federated Learning (DFL) improves with DAQSyn, an adaptive framework reducing synchronization delays and communication costs. This resource-aware strategy enhances model convergence and performance on diverse devices.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Distributed Systems
Background:
- Decentralized Federated Learning (DFL) enables collaborative training on edge devices, prioritizing privacy and autonomy.
- Existing DFL frameworks face challenges like synchronization delays, communication overhead, and poor scalability in heterogeneous environments.
- Conventional methods often use fixed-precision quantization and uniform synchronization, neglecting device and data heterogeneity, which slows convergence and resource use.
Purpose of the Study:
- To introduce the Decentralized Adaptive Quantization-Aware Synchronous (DAQSyn) framework.
- To present a unified, resource-aware learning strategy integrating adaptive quantization, synchronization, and performance-weighted aggregation.
- To address limitations of existing DFL methods by improving efficiency and performance on diverse systems.
Main Methods:
- Implementing adaptive quantization where devices adjust model precision based on their capabilities.
- Utilizing barrier synchronization (BSP-based) to harmonize update timing and reduce idle waiting.
- Employing performance-weighted aggregation to prioritize high-quality local updates for enhanced model stability.
Main Results:
- DAQSyn reduces idle waiting time by ~19% (vs. FP16) and >25% (vs. low-bit quantization).
- Achieved 1% accuracy improvement and 2.5x faster convergence compared to fixed-precision methods.
- Demonstrated robust learning performance and reduced communication costs across heterogeneous devices and non-IID data.
Conclusions:
- The DAQSyn framework offers a significant advancement in Decentralized Federated Learning.
- Adaptive quantization and synchronization mechanisms effectively improve convergence speed and resource utilization.
- DAQSyn provides a scalable and efficient solution for collaborative learning in diverse, real-world edge computing environments.
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