Chirp-Based Majority Vote Computation for Federated Edge Learning and Distributed Localization

Abstract

In this study, we propose an over-the-air computation (OAC) scheme based on chirps to detect the majority votes (MVs) in a wireless network for federated edge learning (FEEL) and distributed localization. With the proposed approach, a group of votes is mapped to an index of a linear chirp at each edge device (ED). From superposed chirp signals, the corresponding MVs at the edge server (ES) are then detected non-coherently with a set of energy comparators by exploiting the bit representation of the indices. The proposed scheme is power-efficient and has low out-of-band emission while it does not use the channel state information (CSI) at the EDs and ES. Hence, it paves the way for long-distance FEEL and distributed localization based on MVs in a wireless sensor network with low-complexity devices. For FEEL, we comprehensively demonstrate the efficacy of the proposed approach under heterogeneous data distribution. For localization, we propose iterative refinements and multiple repetitions to improve the localization performance. We show that the proposed strategies minimize the distance between the root-mean-square error (RMSE) error and quantization bound.

Keywords

• Chirp
• distributed learning
• distributed localization
• over-the-air computation
• PMEPR