Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

no code implementations • 18 Oct 2021 • Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, Pu Wang

Federated Learning (FL) over wireless multi-hop edge computing networks, i. e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm.

Edge-computing Federated Learning +3

EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge

no code implementations • 14 Oct 2021 • Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, Pu Wang

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.

Edge-computing Federated Learning +1