EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
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DOI：
10.1016/j.comnet.2022.109396
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Pinyarash Pinyoanuntapong;Prabhu Janakaraj;Ravikumar Balakrishnan;Minwoo Lee;Chen Chen-Chen;Pu Wang
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文献类型：
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作者：
Pinyarash Pinyoanuntapong;Prabhu Janakaraj;Ravikumar Balakrishnan;Minwoo Lee;Chen Chen-Chen;Pu Wang
联合学习(FL)是一种用于下一代人工智能系统的分布式机器学习技术，它允许多个工作者(即边缘设备)协作学习共享的全局模型，同时将他们的数据保存在本地以防止隐私泄露。在无线多跳网络上启用FL可以使人工智能大众化，并使其能够以经济高效的方式访问。然而，噪声带宽受限的多跳无线连接可能会导致延迟和游动的模型更新，这会显著降低FL收敛速度。为了应对这样的挑战，本文旨在通过优化多跳联合组网性能来加速无线边缘上的FL融合。特别地，FL收敛优化问题被描述为马尔可夫决策过程(MDP)。为了解决这类MDP问题，提出了多智能体强化学习(MA-RL)算法和特定于域的动作空间优化方案，在线学习延迟最小的转发路径，以最小化边缘设备(即工作者)和远程服务器之间的模型交换延迟。为了验证提出的解决方案，开发并实现了EdgeML，这是文献中第一个在多跳无线边缘计算网络上实现FL的实验框架。EdgeML允许我们在实际无线设备中快速原型、部署和评估新的FL算法以及基于RL的系统优化方法。此外，通过定制广泛采用的Linux无线路由器和ML计算节点，实现了一个物理实验实验台。这样的试验台可以为FL在现场的实际表现提供有价值的见解。实验结果表明，与生产级商用无线网络协议Batman-Adv支持的FL系统相比，本文提出的基于网络加速的FL系统能够显著提高FL收敛速度。
Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a shared global model while keeping their data locally to prevent privacy leakage. Enabling FL over wireless multi-hop networks can democratize AI and make it accessible in a cost-effective manner. However, the noisy bandwidth-limited multi-hop wireless connections can lead to delayed and nomadic model updates, which significantly slows down the FL convergence speed. To address such challenges, this paper aims to accelerate FL convergence over wireless edge by optimizing the multi-hop federated networking performance. In particular, the FL convergence optimization problem is formulated as a Markov decision process (MDP). To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i.e., workers) and the remote server. To validate the proposed solutions, EdgeML is developed and implemented, which is the first experimental framework in the literature for FL over multi-hop wireless edge computing networks. EdgeML allows us to fast prototype, deploy, and evaluate novel FL algorithms along with RL-based system optimization methods in real wireless devices. Moreover, a physical experimental testbed is implemented by customizing the widely adopted Linux wireless routers and ML computing nodes. Such testbed can provide valuable insights into the practical performance of FL in the field. Finally, our experimentation results on the testbed show that the proposed network-accelerated FL system can practically and significantly improve FL convergence speed, compared to the FL system empowered by the production-grade commercially-available wireless networking protocol, BATMAN-Adv.