To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices

TL;DR

This paper proposes a delay-efficient federated learning mechanism for mobile edge devices that optimizes the trade-off between communication and computation to reduce overall training time.

Contribution

It introduces an optimization-based approach to balance communication and computation delays in federated learning over mobile edge devices.

Findings

Reduces total training delay through optimized communication-computation trade-off

Demonstrates effectiveness via extensive simulations

Improves convergence time in unreliable network conditions

Abstract

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.