Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning
By Umair Mohammad, Sameh Sorour. 2019
17 citations recorded by the source corpus.
This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterogeneous computing and communication capacities. The designed approach considered in this paper ensures that all devices work for a certain duration that covers the time for data/model distribution, learning iterations, model collection and global aggregation. The resulting problem is an integer non-convex program with quadratic equality constraints as well as linear equality and inequality constraints. Because the problem is NP-hard, we relax the integer constraints in order to solve it efficiently with available solvers. Analytical bounds are derived using the KKT conditions and Lagrangian analysis in conjunction with the suggest-and-improve approach. Results show that our approach reduces the gradient staleness and can offer better accuracy than the synchronous scheme and the asynchronous scheme with equal task allocation.
Subfields: Artificial Intelligence, Computer Networks and Communications, Federated & Distributed Learning, ML Systems & Hardware
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Citations in the corpus
- Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2019, background): “Federated learning (FL) is a recent advance in distributed ML in which data are acquired and processed locally at the client side, and then the updated ML parameters are transmitted to a central server for aggregation [6]–[8].”
- A review of applications in federated learning (2020, methodology): “…training has already become the major form for FL due to superior performance of SGD in the central server settings compared to asynchronous way (Chen, Ning and Rangwala, 2019; Mohammad and Sorour, 2019) Prior optimization of FL mainly focusses on evolution of FedAvg in a synchronous fashion.”