用联邦强化学习优化智能眼镜的AI实时运行性能。
Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears
- 通过联邦强化学习实现多设备协同训练，保护数据隐私。
- 异步联邦策略使性能波动降低，提升系统稳定性。
- 适合需要实时可靠AI处理的智能穿戴设备场景。
扩展现实技术正在重塑医疗、娱乐和教育等领域，智能眼镜（SEWs）与人工智能（AI）在此过程中发挥关键作用。然而，智能眼镜在计算能力、内存和电池寿命方面存在固有限制，而将计算任务外传至外部服务器又受限于网络状况和服务器负载波动。为应对这些挑战，我们提出一种联邦强化学习（FRL）框架，允许多个智能体在保护数据隐私的前提下协同训练。我们实现了同步与异步联邦策略，模型聚合可在固定周期或根据智能体进展动态触发。实验结果表明，联邦智能体表现出显著更低的性能波动，确保了更高的稳定性和可靠性。这些发现凸显了FRL在需要鲁棒实时AI处理的应用中的潜力，例如智能眼镜中的实时目标检测。
原文摘要 · Abstract (English)
Extended reality technologies are transforming fields such as healthcare, entertainment, and education, with Smart Eye-Wears (SEWs) and Artificial Intelligence (AI) playing a crucial role. However, SEWs face inherent limitations in computational power, memory, and battery life, while offloading computations to external servers is constrained by network conditions and server workload variability. To address these challenges, we propose a Federated Reinforcement Learning (FRL) framework, enabling multiple agents to train collaboratively while preserving data privacy. We implemented synchronous and asynchronous federation strategies, where models are aggregated either at fixed intervals or dynamically based on agent progress. Experimental results show that federated agents exhibit significantly lower performance variability, ensuring greater stability and reliability. These findings underscore the potential of FRL for applications requiring robust real-time AI processing, such as real-time object detection in SEWs.
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