FedAir: Towards Multi-hop Federated Learning Over-the-Air

Federated learning (FL) has emerged as a key technology for enabling next-generation AI at scale. The classical FL
systems use single-hop cellular links to deliver the local models
from mobile workers to edge routers that then reach the remote
cloud servers via high-speed Internet core for global model
averaging. Due to the cost-efficiency, wireless multi-hop networks
have been widely exploited to build communication backbones.
Therefore, enabling FL over wireless multi-hop networks can
make it accessible in a low-cost manner to everyone (e.g.,
under-developed areas and disaster sites). Wireless multi-hop
FL, however, suffers from profound communication constraints
including noisy and interference-rich wireless links, which results
in slow and nomadic FL model updates. To address this, we
suggest novel machine learning-enabled wireless multi-hop FL
framework, namely FedAir, that can greatly mitigate the adverse
impact of wireless communications on FL performance metrics
such as model convergence time. This will allow us to fast
prototype, deploy, and evaluate FL algorithms over ML-enabled,
programmable wireless router (ML-router). The experiments on
the deployed testbed validate and show that wireless multi-hop
FL framework can greatly accelerate the runtime convergence
speed of the de-facto FL algorithm, FedAvg.