Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

By Umair Mohammad, Sameh Sorour, Mohamed S. Hefeida. 2020

11 citations recorded by the source corpus.

This paper proposes a framework to support federated learning or mobile edge learning (MEL) when there are both, delay constraints on the learning process and constraints on the energy consumed by each device. The aim is to maximize learning accuracy while guaranteeing that the total time taken and energy consumed by each learner in the system are bounded by a preset duration and maximum energy, respectively, while taking into account heterogeneous communication and communication capabilities of the channels and nodes. The problem of interest is shown to be NP-hard and a suggest-and-improve (SAI) approach is proposed based on the solution of the Lagrangian Dual problem (suggest) followed by a local optimizer based on coordinate descent (the improve step). The merits of this proposed solution, which is heterogeneity aware (HA), are exhibited by comparing its performances to both numerical approaches and the heterogeneity unaware (HU) approach.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, background): “In contrast, the works of [10], [11] and investigates the impact of maximizing the number of local updates on the learning accuracy by optimizing the size or portion of the local dataset used.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “an overall offloading target, such as offloading latency minimization [44].”