Multi-Resource Allocation for On-Device Distributed Federated Learning Systems

TL;DR

This paper presents a multi-resource allocation scheme for on-device federated learning systems that optimizes latency and energy consumption by solving a decomposed convex optimization problem with closed-form solutions.

Contribution

It introduces a novel distributed resource allocation method leveraging Lagrangian duality and harmony search, providing optimal solutions with insights into resource tradeoffs.

Findings

The proposed algorithm achieves near-optimal resource utilization.

Numerical results validate the effectiveness of the resource allocation scheme.

The method reduces latency and energy consumption in federated learning systems.

Abstract

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.