Adaptive Task Allocation for Mobile Edge Learning
By Umair Mohammad, Sameh Sorour. 2018
33 citations recorded by the source corpus.
This paper aims to establish a new optimization paradigm to efficiently execute distributed learning tasks on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computation on heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadratically-constrained integer linear problem. Being NP-hard, the paper relaxes it into a non-convex problem over real variables. We then propose a solution based on deriving analytical upper bounds on the optimal solution of this relaxed problem using KKT conditions. The merits of this proposed solution is exhibited by comparing its performances to both numerical approaches and the equal task allocation 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
- Medhi Bennis
- Jihong Park
- Anis Elgabli
- Sumudu Samarakoon
- Merouane Debbah
- Seong-Lyun Kim
- Umair Mohammad
- Sameh Sorour
In Computer Networks and Communications
- Medhi Bennis
- Jihong Park
- Merouane Debbah
- Sumudu Samarakoon
- Anis Elgabli
- Seong-Lyun Kim
- Umair Mohammad
- Sameh Sorour
Citations in the corpus
- Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, methodology): “The general model for distributed ML and the MEL model for the synchronous case have both been discussed and welldefined in [6], [9].”
- Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints (2020, background): “The details of the resulting model sizes and complexities are discussed in [6].”
- Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “Such task offloading in MEC needs to take into the account of device heterogeneity [122], communication limitations [123], [124], and demand-supply capabilities of processing power [106] in addition to its impact on the tolerable training latency [121] and target training/inference accuracy [125] wh”