EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge

Abstract

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 costeffective manner. However, the noisy bandwidth-limited multihop 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, multiagent 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, FedEdge is developed and implemented, which is the first experimental framework in the literature for FL over multihop wireless edge computing networks. FedEdge 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 networkaccelerated 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. I. INTRODUCTION:Distributed machine learning, specifically federated learning (FL), has been envisioned as a key technology for enabling next-generation AI at scale. FL significantly reduces privacy risks and communication costs, which are critical in modern AI systems. FL allows workers (i.e., edge devices) to collaboratively learn a global model and maintains the locality