Decentralized learning for wireless communications and networking
This work addresses the problem of efficient and private data processing in decentralized wireless networks for applications like sensor networks and cognitive radio, though it is incremental as it builds on existing ADMM methods.
The paper tackles decentralized learning for processing graph data in wireless networks by formulating a separable optimization problem and solving it with ADMM to enable parallelization without sharing training data. It demonstrates applications in target tracking, traffic anomaly detection, power system estimation, and spectrum cartography, achieving global inference accuracy comparable to centralized methods while keeping communication costs low.
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping inter-node communications at affordable levels, the local (per-node) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.